Multi-parameter diabetes risk evaluations

ABSTRACT

Methods, systems and circuits evaluate a subject&#39;s risk of developing type 2 diabetes or having prediabetes using at least one defined mathematical model of risk of progression that can stratify risk for patients having the same glucose measurement. The model may include NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject.

RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. ProvisionalApplication Ser. No. 61/657,315, filed Jun. 8, 2012, U.S. ProvisionalApplication Ser. No. 61/711,471, filed Oct. 9, 2012, U.S. ProvisionalApplication Ser. No. 61/739,305, filed Dec. 19, 2012, and U.S. patentapplication Ser. No. 13/830,784, filed Mar. 14, 2013, the contents ofwhich are hereby incorporated by reference as if recited in full herein.

FIELD OF THE INVENTION

The present invention relates generally to analysis of in vitrobiosamples. The invention may be particularly suitable for NMR analysisof in vitro biosamples.

BACKGROUND OF THE INVENTION

Type 2 diabetes mellitus (T2DM or “diabetes”) is one of the most costlyand burdensome chronic diseases in the U.S. and other countries. Thedefining feature of T2DM is hyperglycemia, a reflection of impairedcarbohydrate (glucose) utilization resulting from a defective ordeficient insulin secretory response. T2DM is a late manifestation ofmetabolic derangements that begin many years earlier. Its cause isbelieved to be a progressive increase in insulin resistance coupled withdeteriorating β-cell function. So long as the pancreatic β-cells areable to secrete enough insulin to compensate for the progressiveresistance of target tissues to insulin's hypoglycemic effects, thepatient is able to maintain normal fasting glucose levels. Hyperglycemiaand the transition to T2DM occur as a consequence of progressive β-celldysfunction which leads to failure to maintain hypersecretion of insulinin the face of increasing insulin resistance.

Type 2 diabetes has been traditionally diagnosed by the detection ofelevated levels of glucose (sugar) in the blood (hyperglycemia). Whilehyperglycemia defines diabetes, it is a very late stage development inthe chain of events that lead from insulin resistance to full-blowndiabetes. Accordingly, it would be desirable to have a way ofidentifying whether or not a subject is at risk for developing Type 2diabetes (i.e., is predisposed to the condition) prior to thedevelopment of the classic symptoms, such as hyperglycemia. Earlierdetection of indicators of the disease (e.g., detection before glucoselevels are elevated enough to be considered hyperglycemia) may lead tomore effective treatment of the disease, if not actual prevention of theonset of the disease.

The most direct and accurate methods for assessing insulin resistanceare laborious and time-consuming, and thus impractical for clinicalapplication. The “gold standard” among these research methods is thehyperinsulinemic euglycemic clamp, which quantifies the maximal glucosedisposal rate (GDR, inversely proportional to insulin resistance) duringthe clamp. Another arduous research method which is somewhat lessreproducible (CV 14-30%) is the frequently sampled intravenous glucosetolerance test (IVGTT) with minimal model analysis, which measuresinsulin sensitivity (S_(i)), the inverse of insulin resistance.

Risk of progression to Type 2 diabetes is currently assessed primarilyby fasting glucose, with concentrations 100-125 mg/dL defining ahigh-risk “pre-diabetes” condition and for which T2DM is currentlydefined in patients having fasting plasma glucose levels at 126 mg/dLand above. However, the actual risk of individual patients withpre-diabetes (those at greatest risk of developing T2DM in the nearfuture) varies widely.

NMR spectroscopy has been used to concurrently measure low densitylipoproteins (LDL), high density lipoproteins (HDL), and very lowdensity lipoproteins (VLDL), as LDL, HDL and VLDL particle subclassesfrom in vitro blood plasma or serum samples. See, U.S. Pat. Nos.4,933,844 and 6,617,167, the contents of which are hereby incorporatedby reference as if recited in full herein. U.S. Pat. No. 6,518,069 toOtvos et al. describes NMR derived measurements of glucose and/orcertain lipoprotein values to assess a patient's risk of developingT2DM.

Generally stated, to evaluate the lipoproteins in a blood plasma and/orserum sample, the amplitudes of a plurality of NMR spectroscopy derivedsignals within a chemical shift region of NMR spectra are derived bydeconvolution of the composite methyl signal envelope to yield subclassconcentrations. The subclasses are represented by many (typically over60) discrete contributing subclass signals associated with NMR frequencyand lipoprotein diameter. The NMR evaluations can interrogate the NMRsignals to produce concentrations of different subpopulations, typicallyseventy-three discrete subpopulations, 27 for VLDL, 20 for LDL and 26for HDL. These sub-populations can be further characterized asassociated with a particular size range within the VLDL, LDL or HDLsubclasses.

An advanced lipoprotein test panel, such as the LIPOPROFILE® lipoproteintest, available from LipoScience, Raleigh, N.C., has typically includeda total high density lipoprotein particle (HDL-P) measurement (e.g.,HDL-P number) that sums the concentration of all the HDL subclasses anda total low density lipoprotein particle (LDL-P) measurement that sumsthe concentration of all the LDL subclasses (e.g., LDL-P number). TheLDL-P and HDL-P numbers represent the concentration of those respectiveparticles in concentration units such as nmol/L. LipoScience has alsodeveloped a lipoprotein-based insulin resistance and sensitivity index(the “LP-IR™” index) as described in U.S. Pat. No. 8,386,187, thecontents of which are hereby incorporated by reference as if recited infull herein.

Despite the foregoing, there remains a need for evaluations that canpredict or assess a person's risk of developing type 2 diabetes beforethe onset of the disease.

SUMMARY

Embodiments of the invention provide risk assessments of a subject'srisk of developing type-2 diabetes in the future using a multi-parameter(multi-variate) model of defined predictive biomarkers.

The risk assessments can generate diabetes risk index scores thatstratify risk beyond glucose measurements alone and may be decoupledfrom glucose measurements. The glucose measurements, where used, canhelp establish a timeline of conversion to type 2 diabetes. The diabetesrisk index scores when used without glucose information may reflect riskover a longer term associated with underlying metabolic issues.

The multi-variate risk progression model can include at least onedefined lipoprotein component, at least one defined branched chain aminoacid and at least one inflammatory biomarker.

The multi-variate model can be used for assessing patients for or duringclinical trials, during a therapy or therapies, for drug development,and/or to identify or monitor anti-obesity drugs or other drug therapycandidates.

The multi-variate model can include at least one of the following: NMRmeasurements of GlycA, Valine, and a plurality of lipoprotein components(e.g., subclasses) derived from the same NMR spectrum.

The at least one lipoprotein component of the defined mathematical modelof risk may include a first interaction parameter of the measurement ofGlycA multiplied by a concentration of a defined subpopulation of highdensity lipoprotein (HDL) particles. The model can also or alternativelyinclude a second interaction parameter of HDL size multiplied by theconcentration of the defined HDL subpopulation.

The HDL subpopulation can include only medium HDL particle subclasseswith diameters between about 8.3 nm (average) to about 10.0 nm(average).

Embodiments of the invention include, methods, circuits, NMRspectrometers or NMR analyzers, and processors that evaluate a futurerisk of developing diabetes and/or risk stratification for those having“prediabetes” by evaluating NMR spectra of an in vitro blood plasma orserum patient sample using a defined multi-component risk progressionmodel.

The NMR signal can have a peak centered at about 2.00 ppm for GlycA.

The diabetes risk index can be calculated using a mathematical model ofrisk that generates a single score representing future risk ofdeveloping type 2 diabetes in a numerical range of numbers reflectingrisk from about 0-80% or 0-100%.

The diabetes risk index can include lipoprotein components and at leastone of GlycA and Valine.

The lipoprotein components can include at least one of (i) a ratio ofmedium to total high density lipoprotein particle (HDL-P) number and(ii) VLDL size.

Yet other embodiments are directed to a patient report that includes adiabetes risk index (DRI) showing a percentage (e.g., from 0-100) ofrisk of diabetes conversion rate in the future over based on a studypopulation (evaluated over a 1-25 year period or other period, e.g., 1,2, 3, 4, 5, 6, 7, 8, 9, 10 or 10-15 year risk window) based on a glucoselevel and an associated quartile or quintile of the patient's DRI riskscore relative to a defined population. The patient report can includethe patient's risk and a comparative risk of a population with a loweror higher quartile or quintile DRI score and the same glucose.

The DRI risk score can be calculated using a plurality of NMR derivedmeasurements including: lipoprotein measurements, a measure of GlycA inμmol/L and/or arbitrary units, and optionally a measure of Valine inμmol/L units.

Embodiments of the invention include a method of evaluating a subject'srisk of developing type 2 diabetes and/or of having prediabetes. Themethods include programmatically calculating a diabetes risk index scoreof a subject using at least one defined mathematical model of risk ofdeveloping type 2 diabetes that includes at least one lipoproteincomponent, at least one branched chain amino acid and at least oneinflammatory biomarker obtained from at least one in vitro biosample ofthe subject.

In some embodiments, the at least one defined mathematical model of riskmay include NMR derived measurements of a plurality of selectedlipoprotein components of the at least one biosample of the subject, andNMR measurements of at least one of GlycA and Valine. The definedmathematical risk model may include only NMR derived measurements of asubject's in vitro blood plasma or serum biosample.

In some embodiments, the method may include programmatically defining atleast two different mathematical models of risk of developing type 2diabetes, the at least two different mathematical models including onefor subjects on a statin therapy that includes lipoprotein componentsthat are statin insensitive and one for subjects not on a statin therapywith at least one different lipoprotein component.

In some embodiments, the method may include programmatically defining atleast two different mathematical models of risk of developing type 2diabetes. The at least two different mathematical models can havedifferent lipoprotein components including one for fasting biosamplesand one for non-fasting biosamples.

In some embodiments, the method may include programmatically generatinga report with a graph of risk of progression to type 2 diabetes in thefuture over (e.g., a 1-7 year period) as a function of different rangesof glucose level, showing the risks for those in different quartiles,quintiles or deciles of the diabetes risk index score. In someembodiments, the graph shows references of at least first (low) and high(e.g, fourth quartile, fifth quintile or 10th decile) DRI scores basedon a defined population to thereby allow for ease of identifying orunderstanding risk stratification.

In some embodiments, the method can include programmatically evaluatinga fasting blood glucose measurement of the subject using at least one invitro biosample. The diabetes risk index score can be a numerical scorewithin a defined score range, with scores associated with a fourthquartile (4Q) or fifth quintile (5Q) of a population norm reflecting anincreased or high risk of developing type 2 diabetes within 5-7 years.The method can include programmatically identifying respective subjectsthat are at increased risk of developing type 2 diabetes prior to onsetof type-2 diabetes when fasting blood glucose levels are between 90-110mg/dL and the diabetes risk score is in the 4Q or 5Q range.

In some embodiments, the method may include evaluating a fasting bloodglucose measurement of the subject, wherein the diabetes risk indexscore is a numerical score within a defined score range, with scoresassociated with a fourth quartile (4Q) or fifth quintile (5Q) of apopulation norm reflecting an increased or high risk of developing type2 diabetes within 5-7 years.

The method can include programmatically identifying respective subjectsthat are at increased risk of developing type 2 diabetes prior to onsetof type-2 diabetes when fasting blood glucose (FPG) levels are between90-125 mg/dL. The programmatically identifying can be carried out tostratify risk in subjects having the same FPG and a different diabetesrisk score.

In some embodiments, the method can include, before the programmaticcalculation, placing a blood plasma or serum sample of the subject in anNMR spectrometer; obtaining at least one NMR spectrum of the sample;deconvolving the obtained at least one NMR spectrum; and calculating NMRderived measurements of GlycA and a plurality of selected lipoproteinparameters based on the deconvolved at least one NMR spectrum. Thecalculating step may be carried out to also calculate a measurement ofbranched chain amino acid Valine.

In some embodiments, the diabetes risk index score may have a definednumerical range. The method can include programmatically generating areport that identifies a respective subject as at risk of developingprediabetes if a fasting blood plasma or serum glucose value is below100, e.g., between about 80-99 mg/dl (or even lower) and the diabetesrisk index is in a fourth quartile, fifth quintile and/or top decile ofa population norm.

In some embodiments, the defined at least one mathematical model mayinclude NMR measurements of GlycA and a plurality of selectedlipoprotein components using lipoprotein subclasses, sizes andconcentrations measured from an in vitro blood plasma or serumbiosample.

In some embodiments, the at least one defined mathematical model may beselected lipoprotein components comprising at least two of thefollowing: large VLDL subclass particle number, medium VLDL subclassparticle number, total HDL subclass particle number, medium HDL subclassparticle number and VLDL particle size.

The selected lipoprotein components may include all of the listedlipoprotein components.

In some embodiments, the at least one defined mathematical model mayinclude a ratio of medium HDL-P to total HDL-P.

The at least one defined mathematical model may, in some embodiments,include VLDL subclass particle size (vsz3), a ratio of medium HDL-P tototal HDL-P (HMP_HDLP) multiplied by GlycA and a ratio of VLDL size by asum of large VLDL-P and medium VLDL-P.

Before the programmatic calculation, in some embodiments, the method mayinclude electronically obtaining a composite NMR spectrum of a GlycAfitting region of the biosample of the subject, wherein the GlycAfitting region extends from 1.845 ppm to 2.080 ppm, and wherein theGlycA peak region is centered at 2.00 ppm; electronically deconvolvingthe composite NMR spectrum using a defined deconvolution model with highdensity lipoprotein (HDL) components, low density lipoprotein (LDL)components, VLDL (very low density lipoprotein)/chylomicron components,and curve fit functions associated with at least a GlycA peak region;and programmatically generating a measure of GlycA using the curve fitfunctions. The method may further include applying a conversion factorto the measure of GlycA to provide the measure in μmol/L.

In some embodiments, the curve fit functions may be overlapping curvefit functions. The measure of GlycA may be generated by summing adefined number of curve fit functions. The deconvolution model mayfurther comprise a protein signal component for protein having a densitygreater than 1.21 g/L.

Before the programmatic calculation, in some embodiments, the method mayinclude electronically obtaining an NMR spectrum of a Valine fittingregion of the biosample of the subject; electronically identifying aValine signal as located upstream or downstream a defined number of datapoints of a peak of a defined diluent in the biosample; electronicallydeconvolving the composite NMR spectrum using a defined deconvolutionmodel; and electronically quantifying Valine using the deconvolved NMRspectrum.

In some embodiments, the at least one defined mathematical model mayinclude a plurality of different defined models, including one thatincludes lipoprotein components that are insensitive to statin therapy,one that includes lipoprotein components that are sensitive to statintherapy, one that is for fasting biosamples and one that is fornon-fasting biosamples.

Certain embodiments of the present invention are directed to a circuitconfigured to determine whether a patient is at-risk for developing type2 diabetes within the next 5-7 years and/or whether a patient hasprediabetes. The circuit includes at least one processor configured toelectronically calculate a diabetes risk index based on at least onemathematical model of risk to convergence to type 2 diabetes within 5-7years that considers at least one lipoprotein component, at least onebranched chain amino acid and GlycA from at least one in vitro biosampleof the subject.

The at least one mathematical model of risk, in some embodiments, mayinclude NMR derived measurements of GlycA, Valine and a plurality oflipoprotein components.

In some embodiments, the at least one processor may be configured todefine at least two different mathematical models of risk of developingtype 2 diabetes. The at least two different mathematical models caninclude a first model for subjects on a statin therapy that includeslipoprotein components that are statin insensitive and a second modelfor subjects not on a statin therapy. The second model can include atleast some lipoprotein components that are different from the firstmodel. The circuit can be configured to identify subject characteristicsto select the appropriate first or second model of risk for thecalculation of the diabetes risk index score.

In certain embodiments, the at least one processor may be configured todefine at least two different mathematical models of risk of developingtype 2 diabetes with different lipoprotein components, the at least twodifferent mathematical models including one for fasting biosamples, andone for non-fasting biosamples. The circuit can identify subjectcharacteristics to select the appropriate mathematical model of risk forcalculation of the diabetes risk index score.

In some embodiments, the at least one processor may be configured togenerate a report with a graph of risk of progression to type 2 diabetesin the future over a 1-7 year period versus ranges of fasting glucoselevels and a quartile of risk associated with the diabetes risk indexscore. The graph may include visual references of at least first (low)and fourth (high) quartile DRI scores based on a defined population tothereby allow for ease of identifying or understanding riskstratification.

The at least one processor, in some embodiments, may be configured toevaluate a fasting blood glucose measurement of the subject, wherein thediabetes risk index score is a numerical score within a defined scorerange, with scores associated with a fourth quartile (4Q), fifthquintile (5Q) or 10^(th) decile of a population norm reflecting anincreased or high risk of developing type 2 diabetes. The at least oneprocessor can be configured to identify respective subjects that are atincreased risk of developing type 2 diabetes prior to onset of type-2diabetes when fasting blood glucose levels are between 90-110 mg/dL andthe diabetes risk score is in the 4Q, 5Q or 10^(th) decile range.

In some embodiments, the at least one processor may be configured toevaluate a fasting blood glucose measurement of the subject. Thediabetes risk index score can be a numerical score within a definedscore range, with scores associated with a fourth quartile (4Q), fifthquintile (5Q) or 10^(th) decile of a population norm reflecting anincreased or high risk of developing type 2 diabetes. The at least oneprocessor can be configured to identify respective subjects that are atincreased risk of developing type 2 diabetes prior to onset of type-2diabetes when fasting blood glucose (FPG) levels are between 90-125mg/dL. The at least one processor is configured to generate a reportthat can stratify risk in subjects having the same glucose level and adifferent diabetes risk score.

In some embodiments, the at least one mathematical model may include aplurality of lipoprotein components comprising at least two of thefollowing: large VLDL subclass particle number, medium VLDL subclassparticle number, total HDL subclass particle number, medium HDL subclassparticle number and VLDL particle size. The mathematical model mayinclude all of the listed lipoprotein components.

In some embodiments, one of the lipoprotein components of themathematical model may be a ratio of medium HDL-P to total HDL-P.

In certain embodiments, the at least one mathematical model may includea plurality of lipoprotein components including VLDL subclass particlesize (vsz3), a ratio of medium HDL-P to total HDL-P (HMP_HDLP)multiplied by GlycA and a ratio of VLDL size by a sum of large VLDL-Pand medium VLDL-P.

Certain embodiments of the present invention are directed to a computerprogram product for evaluating in vitro patient biosamples. The computerprogram product includes a non-transitory computer readable storagemedium having computer readable program code embodied in the medium. Thecomputer-readable program code includes computer readable program codethat provides at least one mathematical model of risk to progression totype 2 diabetes over a defined time period. The at least onemathematical model of risk to progression to type 2 diabetes can includea plurality of components, including at least one lipoprotein component,at least one inflammatory marker and at least one branched chain aminoacid; and computer readable program code that calculates a diabetes riskindex associated with a patient's biosample based on the at least onemathematical model of a risk of developing type 2 diabetes.

In some embodiments, the computer readable program code that providesthe at least one mathematical model may include model components of NMRderived measurements of GlycA and Valine.

In some embodiments, the computer program product may include computerreadable program code configured to evaluate a glucose measurement ofthe patient. The computer readable program code that calculates adiabetes risk index can calculate the index as a numerical score withina defined score range, with scores associated with a fourth quartile(4Q), fifth quintile (5Q) or top decile of a population norm reflectingan increased or high risk of developing type 2 diabetes.

The computer program product may further include computer readableprogram code configured to identify respective patients that are atincreased risk of developing type 2 diabetes prior to onset of type-2diabetes when fasting blood glucose levels are between 90-110 mg/dL andthe diabetes risk score is in the 4Q, 5Q or 10^(th) decile range.

In some embodiments, the computer program product may be configured togenerate a report that identifies respective subjects that are atincreased risk of developing type 2 diabetes prior to onset of type-2diabetes when fasting blood glucose (FPG) levels are between 90-125mg/dL. The mathematical model can stratify risk in subjects having thesame glucose level and a different diabetes risk score.

In some embodiments, the at least one mathematical model may include aplurality of lipoprotein components comprising at least two of thefollowing: large VLDL subclass particle number, medium VLDL subclassparticle number, total HDL subclass particle number, medium HDL subclassparticle number and VLDL particle size. The mathematical model mayinclude all of the listed lipoprotein components.

In some embodiments, the mathematical model may include a ratio ofmedium HDL-P to total HDL-P. In some embodiments, the at least onemathematical model may include a plurality of lipoprotein componentsincluding VLDL subclass particle size (vsz3), a ratio of medium HDL-P tototal HDL-P (HMP_HDLP) multiplied by GlycA and a ratio of VLDL size by asum of large VLDL-P and medium VLDL-P.

The computer program product may further include computer readableprogram code that identifies and deconvolves a Valine fitting region ofa composite NMR spectrum blood serum or plasma sample of a subject andgenerates a calculated measurement of Valine; and computer readableprogram code that deconvolves a GlycA fitting region of the compositeNMR spectrum. The computer readable program code can deconvolve thecomposite NMR spectrum using a defined GlycA deconvolution model with(i) high density lipoprotein (HDL) components, (ii) low densitylipoprotein (LDL) components, (iii) VLDL (very low densitylipoprotein)/chylomicron components, (iv) another defined protein signalcomponent and (v) curve fitting functions applied to at least a GlycApeak region and generates a calculated measurement of GlycA.

Still other embodiments are directed to a system. The system includes anNMR spectrometer for acquiring at least one NMR spectrum of an in vitrobiosample and at least one processor in communication with the NMRspectrometer. The at least one processor is configured to determine fora respective biosample using the at least one NMR spectrum a diabetesrisk index score based on at least one defined mathematical model ofrisk to convergence to type 2 diabetes that considers at least onelipoprotein component, at least one branched chain amino acid and atleast one inflammatory biomarker obtained from at least one in vitrobiosample of the subject.

The at least one processor may be configured to deconvolve the at leastone NMR spectrum and generate: (i) an NMR measurement of GlycA: (ii) anNMR measurement of Valine; (iii) NMR measurements of lipoproteinparameters; and (iv) the diabetes risk index using the NMR measurementsof GlycA and Valine as components of the at least one definedmathematical model.

In some embodiments, the at least one processor may be configured todefine at least two different mathematical models of risk of developingtype 2 diabetes. The at least two different mathematical models caninclude one for subjects on a statin therapy that includes lipoproteincomponents that are statin insensitive and one for subjects not on astatin therapy with different lipoprotein components.

In some embodiments, the at least one processor in the system may beconfigured to define at least two different mathematical models of riskof developing type 2 diabetes with different lipoprotein components, theat least two different mathematical models including one for fastingbiosamples, and one for non-fasting biosamples.

The at least one processor in the system, in some embodiments, may beconfigured to generate a report with a graph of risk of progression totype 2 diabetes in the future (e.g., over a 1-7 year period or evenlonger) versus ranges of fasting glucose levels and a quartile of riskassociated with the diabetes risk index score. The graph may includevisual references of at least first (low) and fourth (high) quartile orcorresponding quintiles or deciles of DRI scores based on a definedpopulation to thereby allow for ease of identifying or understandingrisk stratification.

In some embodiments, the defined at least one mathematical model mayinclude NMR measurements of GlycA and a plurality of selectedlipoprotein components using lipoprotein subclasses, sizes andconcentrations measured from an in vitro blood plasma or serumbiosample.

In some embodiments, the at least one defined mathematical model mayinclude selected lipoprotein components comprising at least two of thefollowing: large VLDL subclass particle number, medium VLDL subclassparticle number, total HDL subclass particle number, medium HDL subclassparticle number and VLDL particle size. The selected lipoproteincomponents may include all of the listed lipoprotein components.

In some embodiments, the at least one defined mathematical model mayinclude a ratio of medium HDL-P to total HDL-P.

In some embodiments, the at least one defined mathematical model mayinclude VLDL subclass particle size (vsz3), a ratio of medium HDL-P tototal HDL-P (HMP_HDLP) multiplied by GlycA and a ratio of VLDL size by asum of large VLDL-P and medium VLDL-P.

Other embodiments of the present invention are directed to a patientreport comprising: a diabetes risk index score calculated based on adefined mathematical model of risk of progression to type 2 diabeteswith values above a population norm associated with increased risk, andcomprising a graph showing a percentage in a range of risk of diabetesconversion versus glucose level and an associated quartile or quintileof the patient's DRI risk score relative to a defined population, andoptionally a comparative risk of a population with a lower or higherquartile or quintile DRI score and the same glucose measurement.

Still other embodiments are directed to NMR systems. The systems includea NMR spectrometer; a flow probe in communication with the spectrometer;and at least one processor in communication with the spectrometer. Theat least one processor is configured to: (a) obtain (i) NMR signal of adefined GlycA fitting region of NMR spectra associated with GlycA of ablood plasma or serum specimen in the flow probe; (ii) NMR signal of adefined Valine fitting region of NMR spectra associated with thespecimen in the flow probe; and (iii) NMR signal of lipoproteinparameters; (b) calculate measurements of GlycA, Valine and thelipoprotein parameters; and (c) calculate a diabetes risk index using adefined mathematical model of risk of developing type 2 diabetes and/orhaving prediabetes that uses the calculated measurements of GlycA,Valine and at least a plurality of the lipoprotein parameters.

The at least one processor in the NMR system may include at least onelocal or remote processor the NMR analyzer, wherein the at least oneprocessor is configured to deconvolve at least one composite NMRspectrum of the specimen to generate a measurement of GlycA, Valine andthe lipoprotein parameters.

Additional aspects of the present invention are directed to methods ofmonitoring a patient to evaluate a therapy or determine whether thepatient is at-risk of developing type 2 diabetes or has prediabetes. Themethods include: programmatically providing at least one definedmathematical model of risk of progression to type 2 diabetes thatincludes a plurality of components including NMR derived measurements ofselected lipoprotein subclasses and at least one of Valine or GlycA;programmatically deconvolving at least one NMR spectrum of respective invitro patient blood plasma or serum samples and determining measurementsof lipoprotein subclasses, GlycA and Valine; programmaticallycalculating a diabetes risk index score of the respective patients usingthe at least one defined model and corresponding patient samplemeasurements; and evaluating at least one of (i) whether the diabetesrisk index is above a defined level of a population norm associated withincreased risk of developing type 2 diabetes; or (ii) whether thediabetes risk index is increasing or decreasing over time to therebymonitor change which may be in response to a therapy.

Further features, advantages and details of the present invention willbe appreciated by those of ordinary skill in the art from a reading ofthe figures and the detailed description of the preferred embodimentsthat follow, such description being merely illustrative of the presentinvention. Features described with respect with one embodiment can beincorporated with other embodiments although not specifically discussedtherewith. That is, it is noted that aspects of the invention describedwith respect to one embodiment, may be incorporated in a differentembodiment although not specifically described relative thereto. Thatis, all embodiments and/or features of any embodiment can be combined inany way and/or combination. Applicant reserves the right to change anyoriginally filed claim or file any new claim accordingly, including theright to be able to amend any originally filed claim to depend fromand/or incorporate any feature of any other claim although notoriginally claimed in that manner. The foregoing and other aspects ofthe present invention are explained in detail in the specification setforth below.

As will be appreciated by those of skill in the art in light of thepresent disclosure, embodiments of the present invention may includemethods, systems, apparatus and/or computer program products orcombinations thereof.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a graph showing a 5 year conversion risk of developing T2DMbased on fasting glucose levels (mg/dl) and first and fourth quartiles(Q1, Q4, respectively) of a diabetes risk index according to embodimentsof the present invention. The data in the table represent the percent ofsubjects in the 1^(st) and 4^(th) quartiles of six glucose subgroupsthat convert to type 2 diabetes over a 5 year period.

FIG. 1B is a graph showing a diabetes conversion rate (%) to diabetesbased on MESA with upper and lower DRI scores at different glucoseranges according to embodiments of the present invention.

FIG. 2A is a schematic illustration of different lipoprotein subclasspopulations and exemplary size groupings according to embodiments of thepresent invention.

FIG. 2B is a schematic illustration of different lipoprotein subclasscomponents that provide positive and negative risk associations such asthose used to assess insulin resistance and CHD risk according toembodiments of the present invention.

FIG. 2C is a table of HDL subclasses H1-H26 with subpopulations groupedto optimize risk association with T2DM for intermediate risk patients(patients having glucose between high and low levels) according toembodiments of the present invention.

FIG. 3A shows the diabetes conversion rates (%) for MESA studyparticipants grouped into 4 fasting glucose categories. 411 subjects outof the 4985 individuals in the MESA study population converted todiabetes during the 6-year follow-up period. The dotted line divides thestudy population into those with prediabetes, defined by a fastingglucose level >100 mg/dL, and those with normal glucose (≦100 mg/dL).

FIG. 3B shows the diabetes conversion rates (%) for MESA studyparticipants grouped into 3 fasting glucose categories. 411 subjects outof the 4985 individuals in the MESA study population converted todiabetes during the 6-year follow-up period. The dotted lines divide thestudy population into those with low-risk glucose (<90 mg/dL),intermediate-risk glucose (90-110 mg/dL), and high-risk glucose (>110mg/dL) according to embodiments of the present invention.

FIG. 4A is a graph showing the diabetes conversion rates (%) for MESAsubjects with high (5^(th) quintile) and low (1^(st) quintile) DRIscores within each of 4 glucose subgroups, from a study in which 411 outof 4985 total MESA participants converted to diabetes during a 6-yearfollow-up period, according to embodiments of the present invention.

FIG. 4B is a graph showing the diabetes conversion rates (%) for MESAsubjects with high (top decile) and low (bottom decile) DRI scoreswithin each of 4 glucose subgroups, from a study in which 411 out of4985 total MESA participants converted to diabetes during a 6-yearfollow-up period, according to embodiments of the present invention.

FIG. 5 is a graph illustrating diabetes risk associations for 9different size groupings or sub-populations of the 26 HDL subpopulationswith three boxes of further groupings of selected HDL subclassesaccording to embodiments of the present invention. The χ2 values fromthe logistic regression model indicate the strengths and signs of therisk associations as determined in the MESA study population during 6years of follow-up among 4968 MESA participants with 411 incident casesof diabetes diagnosed (all 9 subpopulations were included in the samelogistic regression model, adjusted for age, gender, race, and glucose)according to embodiments of the present invention.

FIG. 6 is a table of DRI prediction model parameters with statisticalmeasures of relevance for an intermediate risk glucose subgroup (e.g.,FPG between 90-110 mg/dL) as contemplated by embodiments of the presentinvention.

FIG. 7 is a graph showing the incremental prediction of incidentdiabetes in MESA (411 out of 4968 participants converting to diabetesduring 6 years) beyond that given by age, gender, race, and glucoselevel, as quantified by the LR χ2 statistic, for 4 different logisticregression models that include, in addition to age, gender, race, andglucose, the variables listed below each of the data bars.

FIG. 8 is a flow chart of exemplary operations that can be used toassess risk of developing T2DM according to embodiments of the presentinvention.

FIG. 9 is an NMR spectrum showing inflammation markers in the plasma NMRspectrum (N-acetyl methyl signals from glycosylated acute phaseproteins) associated with defined NMR markers, GlycA and GlycB,respectively, according to embodiments of the present invention.

FIG. 10A is an example of a fitting function/deconvolution model thatuses four Valine (quartet) signals to calculate NMR measures of Valineaccording to embodiments of the present invention.

FIG. 10B is an expansion of the plasma NMR spectrum containing methylsignals from lipoproteins and branched-chain amino acids according toembodiments of the present invention.

FIG. 10C shows the full NMR spectrum containing methyl signals fromlipoproteins and branched-chain amino acids with an expansion showingthe location of signals from the noted metabolites according toembodiments of the present invention.

FIG. 11A is an NMR spectrum showing glucose signal as multiplets atseveral locations according to embodiments of the present invention.

FIG. 11B is a region of the blood plasma proton NMR spectrum containingglucose peaks according to embodiments of the present invention.

FIGS. 12A and 12B are schematic illustrations of the chemical structuresof the carbohydrate portion of N-acetylglycosylated proteins showing theCH3 group that gives rise to the GlycA NMR signal.

FIGS. 13A and 13B are schematic illustrations of the chemical structuresof the carbohydrate portion of N-acetylneuraminic acid modifiedglycoproteins showing the CH3 group that gives rise to the GlycB NMRsignal.

FIG. 14A is a graph showing an expanded section of the plasma NMRspectrum containing the signal envelope from the plasma lipoproteins andthe underlying GlycA and GlycB signals according to embodiments of thepresent invention.

FIGS. 14B and 14C are graphs of the NMR spectral region shown in FIG.14A illustrating deconvolution models to yield NMR signal formeasurement of GlycA and GlycB according to embodiments of the presentinvention.

FIG. 14D is a table of different components in a GlycA/B deconvolutionmodel according to embodiments of the present invention.

FIG. 14E is an NMR spectrum showing metabolite A present in a sample attypical normal (low) concentration according to embodiments of thepresent invention.

FIG. 14F is an NMR spectrum showing metabolite A present in a sample atan elevated (high) concentration according to embodiments of the presentinvention.

FIGS. 15A-15D are graphs of the GlycA NMR spectral region illustratingspectral overlap from lipoprotein signals (particularly fromVLDL/Chylos) for samples with high TG (triglycerides).

FIG. 16A is a table of different measures of GlycA concentration,depending on a protein component used in the deconvolution (e.g.,“fitting”) model.

FIGS. 16B-16D illustrate the GlycA and GlycB “fits” (deconvolution) ofthe same plasma sample using deconvolution models with different proteincomponents (#1-#3 in the table in FIG. 16A) according to embodiments ofthe present invention.

FIG. 17 is a schematic screen shot of the deconvolution of a 10 mmol/Lreference sample of N-acetylglucosamine, used to generate a conversionfactor relating GlycA and GlycB signal areas to glycoprotein N-acetylmethyl group concentrations according to embodiments of the presentinvention.

FIG. 18A is a flow diagram of an NMR Valine test protocol according toembodiments of the present invention.

FIG. 18B is a flow chart of exemplary pre-analytical processing that canbe used prior to obtaining NMR signal of biosamples according toembodiments of the present invention.

FIG. 18C is a flow diagram of operations that can be used to evaluateValine using NMR according to embodiments of the present invention.

FIG. 19 is a chart of prospective associations of hs-CRP andNMR-measured GlycA and NMR-measured Valine levels with various diseaseoutcomes in MESA (n=5680) according to embodiments of the presentinvention.

FIG. 20 is a chart of characteristics of MESA subjects by NMR measuredGlycA quartile (in “NMR signal area units”) according to embodiments ofthe present invention.

FIG. 21 is a schematic illustration of a system for analyzing apatient's predictable risk using a DRI risk index module and/or circuitusing according to embodiments of the present invention.

FIG. 22 is a schematic illustration of a NMR spectroscopy apparatusaccording to embodiments of the present invention.

FIG. 23 is a schematic diagram of a data processing system according toembodiments of the present invention.

FIG. 24 is a flow chart of exemplary operations that can be used toassess a risk of developing T2DM in the future and/or havingprediabetes, according to embodiments of the present invention.

FIG. 25A is an example of a patient report that includes a GlycAmeasurement and/or a diabetes risk index according to embodiments of thepresent invention.

FIG. 25B is another example of a patient report with a visual (typicallycolor-coded) graphic summary of a continuum of risk from low to highaccording to embodiments of the present invention.

FIG. 26 is a prophetic example of a graph of DRI versus time that can beused to monitor change to evaluate a patient's risk status, change instatus, and/or clinical efficacy of a therapy or even used for clinicaltrials or to contradict planned therapies and the like according toembodiments of the present invention.

FIGS. 27A and 27B are graphical patient/clinical reports of % risk ofdiabetes versus FPG level and DRI score and risk pathway. FIG. 27A showspatient #1's score while FIG. 27B shows patient #1's score in comparisonwith a lesser risk patient (patient number 2) having the same FPG. Whileeach patient has the same FPG, they have different metabolic issuesidentified by the DRI scores stratifying risk according to embodimentsof the present invention.

FIGS. 28A-28C are graphical patient/clinical reports of diabetesconversion rate (%) versus FPG level and DRI score (high DRI, Q4 and lowDRI, Q1) according to embodiments of the present invention. FIG. 28A isfor a 4-year risk of conversion to diabetes. FIG. 28B is a 5-year riskof conversion and FIG. 28C is a 6 year risk of conversion.

FIG. 29 is a graphical patient/clinical report of a Q4/Q1 relative riskof diabetes conversion (1-8) versus FPG level and DRI score for both6-year (upper line) and 2-year conversions periods according toembodiments of the present invention.

FIG. 30 is a graphical patient/clinical report of log scale 5 yearconversion with a diabetes conversion rate (%) versus FPG level and DRIscore (high DRI, Q4 and low DRI, Q1) color coded from green, yellow,pink/orange to red and with a legend textually correlating the risk asvery high, high, moderate and low, according to embodiments of thepresent invention.

FIG. 31 is a graphical patient/clinical report of 5 year conversion todiabetes with a diabetes conversion rate (%) versus FPG level and DRIscore (high DRI, Q4 and low DRI, Q1) color coded from green, yellow,pink/orange to red and with a legend textually correlating the risk asvery high, high, moderate and low, according to embodiments of thepresent invention.

FIG. 32 is a table of data that show the performance of DRI in the IRASdataset, the MESA dataset, and IRAS dataset (using the glucose subgroupsfrom MESA) according to embodiments of the present invention.

FIG. 33 shows the performance of DRI (w/o glucose) in the same datasetcriteria as FIG. 32 according to embodiments of the present invention.

The foregoing and other objects and aspects of the present invention areexplained in detail in the specification set forth below.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The present invention now is described more fully hereinafter withreference to the accompanying drawings, in which embodiments of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art.

Like numbers refer to like elements throughout. In the figures, thethickness of certain lines, layers, components, elements or features maybe exaggerated for clarity. Broken lines illustrate optional features oroperations unless specified otherwise.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. As used herein, phrases such as “between X and Y” and“between about X and Y” should be interpreted to include X and Y. Asused herein, phrases such as “between about X and Y” mean “between aboutX and about Y.” As used herein, phrases such as “from about X to Y” mean“from about X to about Y.”

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the specification andrelevant art and should not be interpreted in an idealized or overlyformal sense unless expressly so defined herein. Well-known functions orconstructions may not be described in detail for brevity and/or clarity.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, components, regions, layersand/or sections, these elements, components, regions, layers and/orsections should not be limited by these terms. These terms are only usedto distinguish one element, component, region, layer or section fromanother region, layer or section. Thus, a first element, component,region, layer or section discussed below could be termed a secondelement, component, region, layer or section without departing from theteachings of the present invention. The sequence of operations (orsteps) is not limited to the order presented in the claims or figuresunless specifically indicated otherwise.

The term “programmatically” means carried out using computer programand/or software, processor or ASIC directed operations. The term“electronic” and derivatives thereof refer to automated orsemi-automated operations carried out using devices with electricalcircuits and/or modules rather than via mental steps and typicallyrefers to operations that are carried out programmatically. The terms“automated” and “automatic” means that the operations can be carried outwith minimal or no manual labor or input. The term “semi-automated”refers to allowing operators some input or activation, but thecalculations and signal acquisition as well as the calculation of theconcentrations of the ionized constituent(s) are done electronically,typically programmatically, without requiring manual input.

The term “about” refers to +/−10% (mean or average) of a specified valueor number.

The term “prediabetes” refers to a risk state for a patient or subjectrather than a disease state. Thus, the term “prediabetes” refers tosomeone that has not been diagnosed with type 2 diabetes and, ascurrently defined by the American Diabetes Association, is associatedwith individuals that have a fasting plasma glucose level that isbetween 100 and 125 mg/dL, an oral glucose tolerance test level that isbetween 140-199 (mg/dL) or an A1C percent that is between 5.7 to 6.4 asrepresented in Table 1 below (the greater the level, the higher the riskof type 2 diabetes for each type of test).

TABLE 1 Blood Test Levels for Diabetes and Prediabetes Oral Glucose A1CFasting Plasma Tolerance Test (percent) Glucose (mg/dL) (mg/dL) Diabetes6.5 or above 126 or above 200 or above Prediabetes 5.7 to 6.4 100 to 125140 to 199 Normal About 5 99 or below 139 or below Definitions: mg =milligram, dL = deciliter For all three tests, within the prediabetesrange, the higher the test result, the greater the risk of diabetes

See, American Diabetes Association. Standards of medical care indiabetes—2012. Diabetes Care. 2012:35 (Supp 1):S12, table 2.

Embodiments of the invention may be particularly suitable to stratifyrisk for patients having the same or similar fasting glucose levels.See, e.g., FIGS. 1A, 1B. Generally stated, it is contemplated that adiabetes risk index score can be used to stratify risk for developingtype 2 diabetes in the future alone or with FPG or other measure ofglucose such as A1C (a non-fasting sample using hemoglobin A1C) or oralglucose tolerance measurements. The diabetes risk score can stratifytype 2 diabetes risk for patients having the same glucose level, butdifferent underlying metabolic situations.

Embodiments of the invention can evaluate a patient's risk of having ordeveloping type 2 diabetes in the future. The risk may be generated withrespect to any suitable timeline, typically stated as within a 5-25 yeartime frame, more typically within about a 5 year or a 6 year time frameusing one or more defined models of conversion to type 2 diabetes in thefuture using a plurality of risk model parameters.

Embodiments of the invention provide new biomarkers that can stratifyrisk of developing type 2 diabetes in the future for patients in anintermediate risk category.

The term “patient” is used broadly and refers to an individual thatprovides a biosample for testing or analysis.

The term “GlycA” refers to a new biomarker that is derived from ameasure of composite NMR signal from carbohydrate portions of acutephase reactant glycoproteins containing N-acetylglucosamine and/orN-acetylgalactosamine moieties, more particularly from the protons ofthe 2-NAcGlc and 2-NAcGal methyl groups. The GlycA signal is centered atabout 2.00 ppm in a plasma NMR spectrum at about 47 degrees C. (+/−0.5degrees C.). The peak location is independent of spectrometer field butmay vary depending on analysis temperature of the biosample and is notfound in urine biosamples. Thus, the GlycA peak region may vary if thetemperature of the test sample varies. The GlycA NMR signal may includea subset of NMR signal at the defined peak region so as to include onlyclinically relevant signal contributions and may exclude a proteincontribution to the signal in this region as will be discussed furtherbelow.

The term “GlycB” refers to a new biomarker that is derived from ameasure of composite NMR signal from the carbohydrate portions of acutephase reactant glycoproteins containing N-acetylneuraminic acid (sialicacid) moieties, more particularly from the protons of the 5-Nacetylmethyl groups. The GlycB signal is centered at about 2.04 ppm in theplasma NMR spectrum at about 47 degrees C. The peak location isindependent of spectrometer field but may vary depending on analysistemperature of the biosample. Thus, the GlycB peak region may vary ifthe temperature of the test sample varies (and is not found in urinesamples).

As used herein, the chemical shift locations (ppm) refer to NMR spectrareferenced internally to CaEDTA signal at 2.519 ppm. Thus, the notedpeak locations discussed and/or claimed herein may vary depending on howthe chemical shift is generated or referenced as is well known to thoseof skill in the art. Thus, to be clear, certain of the described and/orclaimed peak locations have equivalent different peak locations in othercorresponding chemical shifts as is well known to those of skill in theart.

The term “biosample” refers to in vitro blood, plasma, serum, CSF,saliva, lavage, sputum, or tissue samples of humans or animalsEmbodiments of the invention may be particularly suitable for evaluatinghuman blood plasma or serum biosamples, particularly for GlycA and GlycB(which are not found in urine, for example). The blood plasma or serumsamples may be fasting or non-fasting. Where glucose is measured by NMR,the biosample is typically fasting blood plasma or serum samples.However, glucose may be measured by any suitable means.

The terms “population norm” and “standard” refer to values defined by alarge study or studies such as the Framingham Offspring Study or theMulti-Ethnic Study of Atherosclerosis (MESA) or other study having alarge enough sample to be representative of the general population.However, the instant invention is not limited to the population valuesin MESA as the presently defined normal and at-risk population values orlevels may change over time. Thus, a reference range associated withvalues from a defined population in risk segments (e.g., quartiles orquintiles) can be provided and used to assess elevated or reduced levelsand/or risk of having a clinical disease state.

The term “clinical disease state” is used broadly and includes anat-risk medical condition that may indicate medical intervention,therapy, therapy adjustment or exclusion of a certain therapy (e.g.,pharmaceutical drug) and/or monitoring is appropriate. Identification ofa likelihood of a clinical disease can allow a clinician to treat, delayor inhibit onset of the condition accordingly.

As used herein, the term “NMR spectral analysis” means using proton (¹H)nuclear magnetic resonance spectroscopy techniques to obtain data thatcan measure the respective parameters present in the biosample, e.g.,blood plasma or blood serum. “Measuring” and derivatives thereof refersto determining a level or concentration and/or for certain lipoproteinsubclasses, measuring the average particle size thereof. The term “NMRderived” means that the associated measurement is calculated using NMRsignal/spectra from one or more scans of an in vitro biosample in an NMRspectrometer.

The term “downfield” refers to a region/location on the NMR spectrumthat pertains to the left of a certain peak/location/point (higher ppmscale relative to a reference). Conversely, the term “upfield” refers toa region/location on the NMR spectrum that pertains to the right of acertain peak/location/point.

The terms “mathematical model” and “model” are used interchangeably andwhen used with “DRI”, “diabetes risk index”, or “risk”, refer to astatistical model of risk used to evaluate a subject's risk ofdeveloping type 2 diabetes in the future, typically within 2-7 years.The risk model can be or include any suitable model including, but notlimited to, one or more of a logistic model, a mixed model or ahierarchical linear model. The risk models can provide a measure of riskbased on the probability of conversion to type 2 diabetes within adefined time frame, typically within 5-7 years. The risk models may beparticularly suitable for providing risk stratification for patientshaving “intermediate risk” associated with slightly to moderatelyelevated glucose values associated with prediabetes ranges. The DRI riskmodel can stratify a risk of developing T2DM as measured by standard χ2and/or p values (the latter with a sufficiently representative studypopulation).

Embodiments of the invention can include biomarkers that link todiabetic pathophysiology, including two or more of: insulin resistance,impaired β-cell function, inflammation and defective non-insulin (NI)dependent glucose uptake as shown in Table 2.

TABLE 2 BIOMARKERS/PATHOPHYSIOLOGY Diabetic Pathophysiology BioMarker(s)Insulin Resistance LP-IR ™ β-cell Function HDL, Valine InflammationGlycA, HDL Defective NI Glucose Uptake HDL

The role of HDL is complex and HDL-C is considered to be a relativelycrude biomarker. Recently, researchers have suggested that HDL is anactive player in diabetic pathophysiology rather than a bystander. See,Drew et al., The Emerging Roles of HDL in Glucose Metabolism, Nat. Rev.,Endocrinol., 8, 237-245 (2012) published online 24 Jan. 2012. Theproposed HDL biomarkers indicated in the table above refer to definedsubpopulations of HDL which can include negative and positive T2DM riskassociation as will be discussed further below. In some particularembodiments, DRI scores can be generated using DRI risk models that havea plurality of HDL components that represent each of the four differentpathophysiologies recognizing that HDL subclasses play different rolesin a person's risk of developing diabetes.

Inflammation can be associated with many different disease statesincluding, but not limited to T2DM and CHD. It is also believed thatinflammation may modulate HDL functionality. See, e.g., Fogelman, WhenGood Cholesterol Goes Bad, Nature Medicine, 2004. Carbohydratecomponents of glycoproteins can perform biological functions in proteinsorting, immune and receptor recognition, inflammation and othercellular processes.

The DRI model(s) can include at least one inflammatory marker, at leastone lipoprotein component and at least one other defined metabolite orbiomarker. In some embodiments, the DRI models include at least oneinteraction parameter.

The term “interaction parameter” refers to at least two differentdefined parameters combined (multiplied) as a mathematical productand/or ratio. Examples of interaction parameters include, but are notlimited to, medium HDL-P/total. HDL-P, (HM_(DM))(GlycA), (HM_(DM))(HZ),and a ratio of GlycA to medium HDL-P or HM_(DM).

The term “LP-IR™” refers to an insulin resistance score that rates asubject's insulin sensitivity from insulin sensitive to insulinresistant using a summation of risk scores associated with differentdefined lipoprotein components. See, e.g., U.S. Pat. No. 8,386,187 for adetailed discussion of the LP-IR score, the contents of which are herebyincorporated by reference as if recited in full herein. Generallystated, large VLDL, VLDL size, and small LDL have a positive riskassociation while large HDL, LDL size and HDL size have a negativeassociation (FIG. 2B).

The term “lipoprotein component” refers to a component in themathematical risk model associated with lipoprotein particles includingsize and/or concentration of one or more subclasses (subtypes) oflipoproteins. Lipoprotein components can include any of the lipoproteinparticle subclasses, concentrations, sizes, ratios and/or mathematicalproducts (multiplied) of lipoprotein parameters and/or lipoproteinsubclass measurements of defined lipoprotein parameters or combined withother parameters such as GlycA.

The DRI mathematical models can use other clinical parameters such asgender, age, BMI, whether on hypertension medicine and the like.

FIG. 1A is a graph that presents diabetes conversion rates of subjectswithin 6 glucose subgroups. The dotted line connects the data pointsgiving the diabetes conversion rates for all subjects in each of the 6glucose subgroups. The other data points within each glucose subgroupgive the conversion rates for those subjects in the upper and lowerquartiles of the DRI score. As shown, the risk of developing diabetes atany given glucose level is substantially greater for DRI index values inQ4 vs Q1. Notably, the diabetes risk index is a better predictor thanglucose alone because it effectively stratifies risk at any givenglucose level, without requiring any additional clinical informationabout the patient.

FIG. 1B is a graph that illustrates the diabetes risk stratification ofsubjects within each of 4 fasting glucose ranges who have DRI scoresranging from high (DRI=10; top decile) to low (DRI=1; bottom decile). Inthis embodiment, the DRI scores were calculated using a multivariablerisk equation that used the beta-coefficients derived from the logisticregression model shown in FIG. 6 applied to the MESA subpopulationhaving glucose levels from 90-110 mg/dL. The 5 parameters included inthe DRI risk equation were LP-IR, HM×HZ (where HZ refers to HDL size),GlycA, GlycA×HM, and Valine. In this embodiment, glucose was notincluded as one of the terms in the risk equation that generated the DRIscore, but was included in the regression model from which thebeta-coefficients were calculated for the 5 DRI parameters.

A graph such as shown in FIG. 1B can be given to a clinician or producedby an APP (e.g., an application program on a smartphone or electronicnotebook) or other electronic program to help determine the actualdegree of risk of a patient by taking into account both the patient'sDRI score and a previously known or provided glucose level. The glucoselevel can be provided using glucose measurements corresponding to FPG,A1C, or glucose from an oral glucose tolerance test (Table 1).

A person's risk of developing T2DM can be presented as a DRI index scorewith respect to a defined range of risk, from low, intermediate and highrisk. The “index” can be a simple guide or predictor of a person's riskstatus. The diabetes risk index is generated from a statisticallyvalidated mathematical model of risk that can characterize a subject'srisk of developing T2DM in a future timeframe, in a range of from low(e.g., less likely) to high (more likely) relative to a population norm.The “low” value can be associated with DRI values that are in the lowerhalf of a population norm, typically with a 1^(st) quartile or 1^(st)quintile. High risk DRI values can be associated with DM values in afourth quartile or fifth quintile or third tertile or even a 9^(th) or10^(th) decile of a population norm and indicates a high likelihood ofconverting to type 2 diabetes in the future. Intermediate risk DRIvalues can be associated with values between the low and high ranges.

While it is contemplated that the DRI index will be particularly usefulwhen provided as a numerical score, the risk index can be presented on apatient report in different manners. The DRI index can be provided as aresult expressed numerically or alphanumerically, typically comprising anumerical score on a defined scale or within a defined range of values.For example, in particular embodiments, the DRI risk index can beprovided as or include a score within a defined range, such as, forexample, between 0-0.1, 0-1, 0-5, 0-10, 0-24, 0-100, or 0-1000 and thelike. Typically, the lowest number is associated with the least risk andthe higher numbers are associated with increased risk of developing T2DMin the future, typically within 5-7 years, although over time frames maybe used for some embodiments. The lower value in the range may be above“0” such as 1, 2, 3, 4 or 5 and the like, or may even be a negativenumber (e.g., −1, −2, −3, 4, −5 and the like). Other index examples,include, for example, alphanumeric indexes or even icons noting degreesof risk, including but not limited to, “LR1” (low risk), IR5(intermediate risk) and “HR9” (high risk), terms such as “DRI positive”,“DRI high”, “DRI neutral”, “DM low”, “DRI good”, “DRI bad”, “DRI watch”and the like.

As noted above, the diabetes risk index can be decoupled from glucosemeasurements. Thus, for example, a DRI can be calculated for patients asa screening test. If the DRI index (e.g., score) is in an intermediateor high range, a clinician can request a glucose measurement. Thetesting can also be carried out in the reverse. If a patient presentswith an intermediate or high glucose measurement, a DRI test can beordered to stratify risk and/or understand a risk trajectory based onthe DRI score and the glucose level. That is, if a glucose level is low,but a DRI score is elevated, the disease progression can be relativelyearly and a near term risk in the next 1-6 years can be low, but alifetime risk is still of concern. This information may warrantincreased monitoring of DRI and/or glucose and/or influence therapy orlifestyle choices.

To help understand the information provided by the two differentmeasurements, instructional guidelines and/or an electronic program canbe provided to a clinician that generates a test result when both datapoints are supplied. The combined data evaluation can be provided as adownload from a laboratory or from an offering company, such as, forexample, LipoScience (Raleigh, N.C.). Instructional guidelines can beprovided to a clinician so that the clinician can understand the riskstratification provided by the DRI score and can inform a clinicianwhether to order a glucose test which may be more time consuming,expensive or inconvenient for a patient. Thus, the glucose test may beordered less often or only when a patient presents with an intermediateor high DRI risk score. An electronic risk analysis circuit can also beprovided (e.g., a portal accessible via the Internet) that can generaterisk information based on glucose and/or DRI scores (see, e.g.,discussion below with respect to FIG. 21).

The DRI can be generated independent of and/or without requiringconcurrent glucose measurements and may be used to allow a clinician toconsider what risk category a respective patient may belong to.

In other embodiments, the diabetes risk index models may include glucoseas a parameter or may use a glucose measurement as a separate parameterused with a DRI score to characterize risk and/or a risk trajectory andthe glucose and DRI scores can be provided to a clinician as a singletest summary not requiring separate test orders on the part of theclinician.

In some embodiments, for example, where the a patient has a diabetesrisk index that is in the fourth quartile or fourth or fifth quintile ofa population norm, the patient can be identified as at increased riskfor diabetes relative to a population norm.

In some embodiments, the diabetes risk index can be provided without aglucose measurement and/or without including such a measurement in themodel. Thus a DRI score in the fourth quartile or fourth or fifthquintile, alone, can identify those at risk of developing diabetes,typically in the next 5-7 years or lesser or longer timelines asdiscussed above.

In some embodiments, such as where the diabetes risk index is in thethird or fourth quartile or in the fourth or fifth quintile and has aglucose measurement (e.g., for FPG measurement) that is above 95 mg/dl,typically between 100-125 mg/dl, the patient can be identified as atincreased risk or associated with a likelihood of developing T2DM in thefuture, typically within about 5-7 years but other time frames may beutilized. Where glucose measurements are used, FPG glucose measurementsmay be used or A1C or other glucose measurements can be used (see, forexample, Table 1).

As shown with respect to FIGS. 1A and 1B, the risk of developingdiabetes in the future at any given glucose level is substantiallygreater where the DRI value is in Q4 or Q % versus Q1 or at a score of10 versus 1 (or other defined score ranges, depending on the model andscore values employed). Thus, the DRI can be a simple NMR-based riskscore that can effectively stratify risk without requiring additionalclinical information.

Lipoproteins include a wide variety of particles found in plasma, serum,whole blood, and lymph, comprising various types and quantities oftriglycerides, cholesterol, phospholipids, sphyngolipids, and proteins.These various particles permit the solublization of otherwisehydrophobic lipid molecules in blood and serve a variety of functionsrelated to lipolysis, lipogenesis, and lipid transport between the gut,liver, muscle tissue and adipose tissue. In blood and/or plasma,lipoproteins have been classified in many ways, generally based onphysical properties such as density or electrophoretic mobility ormeasures of apolipoprotein A-1 (Apo A-1), the main protein in HDL.

Classification based on nuclear magnetic resonance-determined particlesize distinguishes distinct lipoprotein particles based on size or sizeranges. For example, the NMR measurements can identify at least 15distinct lipoprotein particle subtypes, including at least 5 subtypes ofhigh density lipoproteins (HDL), at least 4 subtypes of low densitylipoproteins (LDL), and at least 6 subtypes of very low densitylipoproteins (VLDL), which can be designated TRL (triglyceride richlipoprotein) V1 through V6.

As shown in FIG. 2A, current analysis methodology allows NMRmeasurements that can provide concentrations of 73 subpopulations with27 VLDL, 20 LDL and 26 HDL subpopulations to produce measurements ofgroups of small and large subpopulations of respective groups. FIG. 2Ashows one example of groupings of the lipoprotein components (such assize groupings for LP-IR™ measurements) but other size groupings may beemployed for other models or model components. For example, to optimizerisk association with type 2 diabetes, different size groupings of HDLsubpopulations can be used as will be discussed further below.

The NMR derived estimated lipoprotein sizes, e.g., HDL-P particle sizesfor H1-H26 (FIG. 2C), noted herein typically refer to averagemeasurements, but other size demarcations may be used.

In preferred embodiments, the DRI risk progression model parameters caninclude NMR derived measurements of deconvolved signal associated with acommon NMR spectrum of lipoproteins using defined deconvolution modelsthat characterize deconvolution components for protein and lipoproteins,including HDL, LDL, VLDL/chylos. This type of analysis can provide for arapid scan acquisition time of under 2 minutes, typically between about20 s-90 s, and corresponding rapid programmatic calculations to generatemeasurements of the model components, then programmatic calculation ofone or more DRI risk scores using one or more defined risk models.

Additionally, in some embodiments, it is contemplated that CPMG (watersuppression) pulse sequences can be used to suppress proteins and revealbranched chain amino acids (BCAA)s such as Valine and/or smallmetabolites that can be quantified when used as components in a DRI riskscore model. As is known to those of skill in the art, Valine is anα-amino acid with the chemical formula HO ₂CCH(NH₂)CH(CH₃)₂. Whenmeasured by NMR, the value can be unitless. The Valine measurement maybe multiplied by a defined conversion factor to convert the value intoconcentration units.

Further, it is also noted that while NMR measurements of the lipoproteinparticles are contemplated as being particularly suitable for theanalyses described herein, it is contemplated that other technologiesmay be used to measure these parameters now or in the future andembodiments of the invention are not limited to this measurementmethodology. It is also contemplated that different protocols using NMRmay be used (e.g., including different deconvolving protocols) in lieuof the deconvolving protocol described herein. See, e.g., Kaess et al.,The lipoprotein subfraction profile: heritability and identification ofquantitative trait loci, J Lipid Res. Vol. 49 pp. 715-723 (2008); andSuna et al., 1H NMR metabolomics of plasma lipoprotein subclasses:elucidation of metabolic clustering by self-organising maps, NMR Biomed.2007; 20: 658-672. Flotation and ultracentrifugation employing adensity-based separation technique for evaluating lipoprotein particlesand ion mobility analysis are alternative technologies for measuringlipoprotein subclass particle concentrations.

FIG. 2B illustrates examples of lipoprotein subclass groupings,including those with concentrations that can be summed to determineHDL-P and LDL-P numbers according to some particular embodiments of thepresent invention. It is noted that the “small, large and medium” sizeranges noted can vary or be redefined to widen or narrow the upper orlower end values thereof or even to exclude certain ranges within thenoted ranges. The particle sizes noted above typically refer to averagemeasurements, but other demarcations may be used.

Embodiments of the invention classify lipoprotein particles intosubclasses grouped by size ranges based on functional/metabolicrelatedness as assessed by their correlations with lipid and metabolicvariables. Thus, as noted above, the evaluations can measure over 20discrete subpopulations (sizes) of lipoprotein particles, typicallybetween about 30-80 different size subpopulations (or even more). FIG.2B also shows these discrete sub-populations can be grouped into definedsubclasses for VLDL and HDL and LDL (IDL can be combined with VLDL orLDL or as a separate category, e.g., with one of the three identified asIDL in the size range between large LDL and small VLDL).

For the GlycA and/or GlycB measurement calculations, where used, thediscrete number of HDL and LDL groupings can be less than those used toquantitatively measure the lipoprotein subclasses. The subclasses ofdifferent size can be quantified from the amplitudes of theirspectroscopically distinct lipid methyl group NMR signals. See,Jeyarajah et al., Lipoprotein particle analysis by nuclear magneticresonance spectroscopy, Clin Lab Med. 2006; 26: pp. 847-870, thecontents of which are hereby incorporated by reference as if recited infull herein. The NMR derived HDL-P and LDL-P particle sizes noted hereintypically refer to average measurements, but other size demarcations maybe used.

The term “LDL-P” refers to a low density lipoprotein particle number(LDL-P) measurement (e.g., LDL-P number) that sums the concentration ofdefined LDL subclasses. Total LDL-P can be generated using a total lowdensity lipoprotein particle (LDL-P) measurement that sums theconcentration (μmol/L) of all the LDL subclasses (large and small)including sizes between 18-23 nm. In some embodiments, the LDL-Pmeasurement may employ selected combinations of the LDL subclasses(rather than the total of all LDL subclass subpopulations).

As used herein, the term “small LDL particles” typically includesparticles whose sizes range from between about 18 to less than 20.5 nm,typically between 19-20 nm. The term “large LDL particles” includesparticles ranging in diameter from between about 20.5-23 nm. It is notedthat the LDL subclasses of particles can be divided in other sizeranges. For example, the “small” size may be between about 19-20.5 nm,intermediate may be between about 20.5-21.2 nm, and large may be betweenabout 21.2-23 nm. In addition, intermediate-density lipoproteinparticles (“IDL” or “IDL-P”), which range in diameter from between about23-29 nm, can be included among the particles defined as “large” LDL (oreven small VLDL). Thus, for example, the LDL subclasses can be between19-28 nm.

The term “HDL-P” refers to a high density lipoprotein particle number(HDL-P) measurement (e.g., HDL-P number) that sums the concentration ofdefined HDL subclasses. Total HDL-P can be generated using a total highdensity lipoprotein particle (HDL-P) measurement that sums theconcentration (μmol/L) of all the HDL subclasses (which may be groupedbased on size into different size categories such as large, medium andsmall) in the size range between about 7 nm (on average) to about 14 nm(on average), typically between 7.4-13.5 nm.

HDL subclass particles typically range (on average) from between about 7nm to about 15 nm, more typically about 7.3 nm to about 14 nm (e.g., 7.4nm-13.5 nm). The HDL-P concentration is the sum of the particleconcentrations of the respective subpopulations of its HDL-subclasses.The different subpopulations of HDL-P can be identified by a number from1-26, with “1” representing the lowest size subpopulation in the HDLsubclass and “26” being the largest size subpopulation in the HDLsubclass. FIG. 2C illustrates estimated HDL size for each HDLlipoprotein component.

For type 2 diabetes risk indexes, the HDL subpopulations can be groupedas H_(DM) groupings as shown in FIG. 2C. The subpopulations can begrouped into small, medium and large, for example. HS_(DM) can includeH3-H8, HM_(DM) can include H9-H17, and HL_(DM) can include H18-H26,respective subpopulations, for example. These size categories wereselected to optimize risk stratification for individuals havingintermediate risk in a population norm (FPG 90-110 mg/dL). That is, thesubclass groups can be selected based on a statistical analysis of studypopulations such as MESA and/or Framingham to determine how the varioussubpopulations should be grouped based on risk association with T2DM(rather than LP-IR or insulin resistance alone as described, forexample, in U.S. Pat. No. 8,386,187, the content of which is herebyincorporated by reference as if recited in full herein).

Thus, in some embodiments, HDL can be identified as a number of discretesize components, e.g., 26 subpopulations (H1-H26) of different sizes ofHDL-P ranging from a smallest HDL-P size associated with H1 to a largestHDL-P size associated with H26. Concentrations of a defined subset ofthe sub-populations can be calculated to generate a HM_(DM) value.Typically HM_(DM) is calculated using some or all of H9-H17 (FIG. 5).Optionally, concentrations of H3-H8 (and optionally H1-H2) can becalculated to generate HS_(DM) and also optionally, concentrations ofsome or all of H18-H26 can be calculated to generate HL_(DM).

The term “large VLDL particles” refers to particles at or above 60 nmsuch as between 60-260 nm. The teem “medium VLDL particles” refers toparticles with sizes between 35-60 nm. The term “small VLDL particles”refers to particles between 29-35 nm. The term “VLDL-P” refers to a verylow density lipoprotein particle number (VLDL-P) measurement (e.g.,VLDL-P number) that sums the concentration of defined VLDL subclasses.Total VLDL-P can be generated by summing the concentrations (nmol/L) ofall the VLDL subclasses (large, medium and small).

As noted above, embodiments of the present invention provide at leastone Diabetes Risk Index (DRI) using one or more defined mathematicalmodels of risk of different defined biomarkers or parameters of an invitro biosample of a patient to identify at-risk patients before onsetof T2DM who may benefit from pharmaceutical, medical, diet, exercise orother intervention.

The DRI evaluation can be decoupled from a glucose measurement and canrelatively easily be generated as a screening tool and may be able toidentify at-risk individuals earlier in time than with conventionaltests.

FIG. 3A is a graph showing diabetes conversion rates (6 year) based onlyon subgroup assignment by fasting glucose level, from the MESA studywith 411 subjects converting to diabetes. This graph reflects currentcategorizations of risk, e.g., low risk below 100 and high risk above100 FPG values. FIG. 3B illustrates division into three categories ofrisk, again, however, using only glucose levels for this categorization.Embodiments of the invention provide DRI scores that can furtherstratify risk of developing type 2 diabetes within any glucose subgroupor at any particular glucose level. The new DRI evaluation may beparticularly useful for those individuals in the intermediate riskrange.

For example, where scores are used, e.g., a DRI score of “1” or in thefirst quartile or quintile (Q1) can indicate an individual that isconsidered low risk as shown in FIGS. 4A and 4B. For a DRI score at thehigh end of the range, e.g., at 5 (Q5 or 5^(th) Quartile) as shown inFIG. 4A or at “10” (which is further refined as an upper 10% of thepopulation norm) as shown in 4B, the individual is considered high risk.FIGS. 1B, 4A and 4B were generated using the model components shown inFIG. 6. FIG. 1A was generated using a different DRI model usingcomponents described in EXAMPLE 1 below. The 1-10 range reflects a finerrisk stratification over FIG. 4A, e.g., the DRI values 1-10 areassociated with 10% increments of the population norm instead of 20%increments.

FIG. 5 is a graph that illustrates HDL subpopulation relations withdiabetes risk, showing those subpopulation groupings with positive(above the “0” line) and negative (below the “0” line) risk associationsaccording to embodiments of the present invention. These groupings arebased on associations with incident diabetes in MESA. Thus, HS_(DM) hasa positive risk association while HM_(DM) and HL_(DM) have negative riskassociations. It is contemplated that some of the subpopulations can beomitted from the noted groupings, e.g., those components having valuesclose to the “0” line in FIG. 5, for example. Exemplary HS_(DM), HM_(DM)and HL_(DM) size ranges were discussed above with respect to FIG. 2C.These size groupings are different from size groupings used for LP-IR,for example.

FIG. 6 is a table that illustrates exemplary DRI prediction modelcomponents according to some embodiments of the present invention. Thetable shows statistical evaluations of the parameters included in alogistic regression model, adjusted for age, gender and race, applied tothe MESA subgroup with glucose levels between 90-110 mg/dL. There were2038 subjects in this subgroup, and 210 converted to diabetes during the6-year follow-up period. While not wishing to be bound to any particulartheory, the table also indicates, for each parameter, a proposedpathophysiologic link to diabetes development. The parameters includeglucose, LP-IR™, HM_(DM) multiplied by HZ, GlycA, GlycA multiplied byHM_(DM) and Valine. The term “HZ” refers to average HDL-size.

FIG. 7 is a chart that shows a statistical evaluation of the predictionof incident diabetes in MESA by 4 different prediction models, beyondthat given by glucose (plus age, gender, and race). As indicated by theincremental LR χ² statistic, the multi-parameter DRI prediction model onthe far right provides almost double the prediction given by a modelincluding insulin, BMI, and hs-CRP or a model with LP-IR alone.

To be clear, FIG. 6 illustrates one particularly suitable set of DRImodel parameters. However, the DRI model can include less than all thoseparameters shown or even other parameters including GlycA and/or GlycB,lipoprotein parameters and other metabolites and/or interactionparameters as will be discussed further below.

Table 3 below summarizes some examples of lipoprotein components thatmay be included in the DRI model.

TABLE 3 MESA, New Diabetes prediction Logistic regression, modelsadjusted on gender, Glucose 90-110 mg/dL age, ethnicity All, N = 411(4983) N = 210 (2038) NMR parameters X2 P X2 P VLDL size 73.5 <0.000127.1 <0.0001 LDL size −51.3 <0.0001 −18.5 <0.0001 HDL size −47.7 <0.0001−12.4 0.0004 Large VLDL 37.9 <0.0001 7.74 0.0054 Medium VLDL 2.57 0.10880.27 Small VLDL −1.72 −2.6 0.1067 IDL −0.017 −0.32 Large LDL −15.47<0.0001 −6.83 0.009 Small LDL 51.4 <0.0001 17.03 <0.0001 Large HDL −45.5<0.0001 −10.31 0.0013 Medium HDL −24.4 <0.0001 −9.5 0.0021 Small HDL43.6 <0.0001 13.1 0.0003

FIG. 8 is a flow chart of exemplary operations that can carry outembodiments of the present invention. As shown, at least one definedmathematical model of risk of progression to type 2 diabetes in a futuretime frame (e.g., 5-7 years) can be provided (block 400). Measurementsof components of the at least one mathematical model of a respectivepatient biosample can be obtained (block 403). A diabetes risk indexscore can be calculated using the model and the measurements (block405). A patient risk report (paper and/or electronic) with the DRI scorecan be provided to desired recipients (e.g., a patient and/or clinician)(block 412). The DRI score can stratify risk for different patientshaving the same glucose measurement (block 404).

The measurements can include NMR measurements of lipoprotein subclassesand either or both GlycA and Valine (block 405). The measurements caninclude one or more of HM_(DM), VLDL size, ratio of medium HDL-P tototal HDL-P and/or sum of VLDL-P (block 408).

A graph of risk of conversion versus glucose level and comparative Q1and/or Q4/Q5 or upper decile references can be provided as a relativerisk summary (block 413).

A graph of risk of conversion versus glucose level and comparativepatient risk with different diabetes risk score measurement and/ordifferent glucose measurement can be provided (block 414).

The risk summary can provide a relative comparison to defined populationnorms.

In some embodiments, values of some or all of the different parametersof the DRI risk model(s) can be derived from a single nuclear magneticresonance (NMR) spectrum of a biosample, typically a fasting bloodplasma or serum sample.

The DRI models can include lipoprotein subclass parameters and GlycA. Insome preferred embodiments, the DRI mathematical model also includes thebranched-chain amino acid Valine. Optionally, glucose may also beconsidered as a risk parameter in the DRI model. Where used, a patient'sglucose measurement may also be obtained from the NMR spectrum of thebiosample or may be obtained in other conventional manners.

The DRI model(s) can incorporate lipoprotein subclass parameters thatare known to be more or less drug-sensitive. The DRI mathematicalmodel(s) can include gender as a variable or may be configured asdifferent models for different genders. The DRI models for a respectivepatient can be electronically adjusted or selected depending on one ormore factors associated with the patient, e.g., gender of the patient,age of the patient, whether the patient is on a certain type ofmedication and the like.

In some embodiments, gender may be a factor in the diabetes risk indexmodel. In some embodiments, age may be a factor in the diabetes riskindex model. In other embodiments, the diabetes risk index can excludeeither gender or age considerations so as to avoid generating falsenegatives or false positives based on data corruption of such ancillarydata not directly tied to a biosample, for example.

The DRI index can be generated in one or more than one way for aparticular patient and two or more DRI indexes can be generated forcomparison, for elective use of one or both by a clinician and/orpresented as a ratio of the two. For example, the NMR data can beevaluated using a plurality of different DRI index calculation modelswhere one model can employ lipoprotein components that are sensitive toa particular drug therapy or class of therapies (e.g., statins) and onethat includes lipoprotein components that are insensitive (or immune) tosuch particular therapy or class of therapies.

TABLE 4 Lipoprotein Components/Parameters Statin Sensitivity/ResistanceAffected by statins - YES Affected by statins - NO VLDL size — NO SmallHDL-P — NO Large VLDL-P Yes Small LDL-P Yes — Large HDL-P Yes LDL sizeYes HDL size Yes Medium HDL-P Yes

By way of another example the DRI risk score can be calculated in two ormore ways, one using components that are more robust where non-fasting(blood plasma or serum) biosamples are analyzed and one that may have abetter risk prediction value but is for fasting biosamples. For example,VLDL components may be reduced or excluded from DRI models fornon-fasting samples. Table 5 lists lipoprotein components that can beaffected by nonfasting.

TABLE 5 Lipoprotein components sensitive to nonfasting. LipoproteinSensitivity to Components Nonfasting LPIR Great Large VLDL Great MediumVLDL Great Small VLDL Great VLDL size Great Small LDL-P Minimal LargeLDL-P Minimal LDL size Minimal Large HDL-P Minimal Medium HDL-P ModerateSmall HDL-P Moderate HDL size Minimal GlycA Minimal Valine MinimalGlucose Great

Similarly, the DRI risk evaluations can be calculated using both thestatin-sensitive and statin-insensitive DRI models allowing theclinician rather than the testing protocol analysis circuit to assesswhich applies to a particular patient. The different values can bepresented in a report or on a display with a comment on the differentresults.

In some embodiments, only one DRI score is provided to the clinicianbased on data electronically correlated to the sample or based onclinician or intake lab input, e.g., fasting “F” or non-fasting “NF,”and statin “S” or non-statin “NS” characterizations of the patient whichdata can be provided on labels associated with the biosample to beelectronically associated with the sample at the NMR analyzer 22 (FIG.22). Alternatively, the patient characterization data can be held in acomputer database (remote or via server or other defined pathway) andcan include a patient identifier, sample type, test type, and the likeentered into an electronic correlation file by a clinician or intakelaboratory that can be accessed by or hosted by the intake laboratorythat communicates with the NMR analyzer 22. The patient characterizationdata can allow the appropriate DRI model to be used for a particularpatient.

HDL-P can be in μmol/L units. VLDL-P and LDL-P can be in units ofnmol/L. Valine and/or GlycA, where used, can be provided as a unitlessmeasure of intensity or one or each can be multiplied by a respectivedefined conversion factor to provide the number in units of μmol/L (see,e.g., FIG. 17 for GlycA).

It is contemplated that a diabetes risk index can be used to monitorsubjects in clinical trials and/or on drug therapies, to identify drugcontradictions, and/or to monitor for changes in risk status (positiveor negative) that may be associated with a particular drug, a patient'slifestyle and the like, which may be patient-specific.

Further discussion of diabetes risk prediction models is provided belowafter discussion of examples of NMR signal deconvolution methods thatcan be used to obtain the GlycA/GycB measurement and, where used, aValine measurement.

FIG. 9 illustrates the resonant peak regions for GA associated withGlycA and GB associated with GlycB in the plasma NMR spectrum. One orboth of these peak regions can include signal that can be defined asinflammation markers in the plasma NMR spectrum.

FIGS. 10A-10C illustrate metabolites that may be quantified. Themetabolites can be quantified in NMR spectra, typically using defineddeconvolution models. The DRI models may include one or more branchedchain amino acids (BCAAs) including one or more of isoleucine, leucine,and Valine (as discussed above) and/or one or more NMR detectablemetabolites such as lactate alanine, acetone, acetoacetic acid, andbeta-hydroxybutyric acid with closely aligned calculated and measuredlines (for contemplated associated deconvolution models) for eachillustrated. The associated centers of peak regions for the respectivemetabolites isoleucine, leucine, Valine, lactate, and alanine are shownin FIG. 10C (0.90 ppm, 0.87 ppm, 1.00 ppm, 1.24 ppm, and 1.54 ppm,respectively).

It is contemplated that other metabolites, which can optionally bemeasured by NMR using a CPMG or other suitable pulse sequence, caninclude choline, phosphocholine, glycine, glycerol, alpha- andbeta-hydroxybutyrate and carnitine.

Recent studies have shown the levels of branched chain amino acids(BCAAs) in serum are associated with the risk of incident diabetes. Wanget al. showed in the Framingham longitudinal cohort that a metabolicsignature composed of BCAAs as well as aromatic amino acids had asignificant correlation with incident diabetes over a span of up to 12years. See, Wang et al., Nature Med., 17, 448, 2011. In a six monthbehavioral/dietary intervention study carried out on 500 obese subjectsthe amount of weight loss was very poorly correlated with improvementsin HOMA score and lipid related factors also showed no significantcorrelation, but the BCAA signature was a strong predictor of improvedinsulin sensitivity. See, Svetkey, et al., JAMA, 299, 1139, 2008 andShah, et al, Diabetologia, 55,321, 2012.

One or more of the set of three BCAAs (valine, leucine and isoleucine)can be quantified by NMR with the use of a CPMG sequence. The term“CPMG” refers to a Carr-Purcel-Meiboom-Gill pulse sequence. This is aseries of phase defined radiofrequency pulses that provide means toattenuate signals from large, rapidly relaxing molecules such asproteins and lipoprotein particles. This sequence involves a series ofradiofrequency pulses in which the signals from the proteins andlipoproteins are attenuated, thereby allowing the detection of a numberof additional metabolite which would be otherwise buried under the largeprotein/lipoprotein signals. A robust signature of all three branchedchain amino acids can be obtained from a biosample with a suitablediluents in a defined percentage relative to the sample, which may be a50:50 mixture of serum to diluents. The BCAA signal can be acquired withabout 90 seconds of acquisition time using a 16 scan CMPG sequence andcan be appended to a standard Lipoprofile® lipoprotein analysis forhigh-throughput analysis. This same 16 scan experiment also containssignals of a number of additional high clinical value metabolitesincluding amino and organic acids.

The BCAA quantification model can include computationally derived basisfunctions for one or each of the three BCAAs as well as experimentalprotein and lipoprotein functions. In modeling the BCAA region of theNMR spectrum, an experimental and/or synthetic model may be used for aBCAA. The baseline may be modeled by CPMG processed experimental spectraof the individual protein and lipoprotein components that are known tocontribute to this region. The model could also use synthetic functionssuch as linear, quadratic & polynomial functions to fit the baselinecomponent. The fitting approach may utilize the Lawson-Hansonnon-negative linear least fitting method to achieve the best agreementbetween the experimental and modeled spectra.

The NMR method can measure all three amino acids in unprocessed serumwithout the need for a calibration standard as would be needed in amass-spectrometry assay. It has been found that the accuratequantitation of these analytes in unprocessed serum biosample can beaffected by the shimming of the spectrometer. Clinical quantitationtherefore may depend upon a more sophisticated BCAA analysis model whichcan simultaneously evaluate a linewidth of a defined reference peak inthe spectrum, such as an EDTA, citrate peak/line or other definedreference peak. Once a linewidth is determined, a predefined correctionfactor or algorithm associated with a respective linewidth can beapplied to generate the quantified BCAA measurement. That is, a set ofdefined correction factors or an algorithm that calculates thecorrections/adjustments for clinical quantification can be identifiedfor different linewidths associated with a particular reference peak andthe BCAA value calculated using curve fit functions can be adjusted bythe defined correction factor.

An “unprocessed biosample” as used herein refers to a biosample that,unlike sample preparation for mass spectrometry analysis, is notsubjected to processing that causes the biosample to be physically orchemically altered after it is obtained (but buffers and diluents can beused). Thus, once the biosample is obtained, components from thebiosample are not altered or removed. For example, once a blood serumbiosample is obtained, the serum is not subjected to processing thatremoves components from the serum. In some embodiments, an unprocessedbiosample is not subjected to filtering and/or ultrafiltrationprocesses.

A respective NMR analyzer 22 (FIG. 22) may be configured to obtain atleast 10 scans per biosample, typically between 10-256 scans, such as 16scans, and possibly ≧96, such as 96 scans or 128 scans with at leastabout 16K data points collected over a 4400 Hz sweep width, per sample,to obtain the NMR data used to measure one or more BCAA. The BCAA scancan be carried out before or after a lipoprotein scan sequence, whichtypically employs a different pulse sequence to allow for quantificationof lipoproteins.

FIGS. 12A/12B illustrates the chemical structure of the carbohydrateportion of N-acetylglycosylated proteins showing the C_(H3) group thatgives rise to the GlycA NMR signal. FIGS. 13A/13B illustrates thechemical structure of the carbohydrate portion of N-acetylneuraminicacid (also called sialic acid) modified glycoproteins showing the C_(H3)group that gives rise to the GlycB NMR signal.

FIG. 10A is an example of a deconvolved signal shown in FIG. 10B with aquartet of Valine signals identified to generate a calculated Vc andmeasured Vm spectrum of the Valine peaks of Valine signal that can beused to measure Valine (in the embodiment shown, the biosample is bloodplasma or serum. One or more of the quartet of peaks of Valine arelocated within a region between about 0.72 ppm to about 1.07 ppm.Typically, one or more of the quartet of peaks of Valine are locatedbetween 0.9 ppm and 1.03 ppm, e.g., at a center of the multiplet regionat about 1.00 ppm of the downfield methyl doublets. One or more of thequartet of peaks of Valine can be used to measure Valine in thebiosample. The concentration of Valine in urine is significantly higherthan that in serum/plasma, and peak position and amplitude will bedifferent.

FIG. 10B is an enlarged region of plasma NMR spectrum (1.07-0.62 ppm)containing methyl signals from lipoproteins and branched-chain aminoacids (leucine, Valine and isoleucine).

As shown in FIG. 10C, the DRI model may include one or more ofisoleucine, leucine, Valine (as discussed above), lactate, and alaninewith closely aligned calculated and measured lines (for contemplatedassociated deconvolution models) for each illustrated. The associatedcenters of peak regions of the respective metabolites are shown in FIG.10C (0.90 ppm, 0.87 ppm, 1.00 ppm, 1.24 ppm, and 1.54 ppm,respectively).

FIG. 11A is an NMR spectrum of a serum sample with a glucose spectrumshown below the upper spectrum. There is a glucose multiplet at 5.2 ppm,and glucose mulitplets centered at 4.6, 3.9, 3.8, 3.7, 3.5, 3.4 and 3.2ppm which could be used for measuring glucose using NMR derivedmeasurements.

FIG. 11B shows the proton NMR spectrum of blood plasma, with the tworegions (region 1 and region 2) containing the signals produced byglucose indicated. In some particular embodiments, the peaks in region 1in the range of 3.81-4.04 ppm can be used for glucose analysis accordingto some embodiments of the present invention. Alternatively, the peaksin region 2 in the range of 3.50-3.63 ppm can be used for the glucoseanalysis of the present invention. Additionally, the combination of thepeaks in region 1 and region 2, may be used for the quantitativedetermination of glucose according to the present invention. The datapoints in the reference or standard spectrum and patient glucose samplespectra are aligned using a line-shape fitting process as describedherein to find the “best fit,” and the intensity of the standardspectrum is scaled to match the sample spectrum. The glucoseconcentration of the standard is multiplied by the scaling factor usedto match the sample lineshape to give the glucose concentration of theblood sample. See, e.g., U.S. Pat. No. 6,518,069 for further discussionof the glucose and lipoprotein measurements for assessing risk ofdeveloping Type 2 diabetes, the contents of which are herebyincorporated by reference as if recited in full herein.

Thus, in some embodiments, a patient glucose measurement can be obtainedvia NMR analysis of the biosample NMR spectrum, along with lipoproteinparticle measurements, GlycA and Valine measurements. However, glucosemeasurements, where used, can alternatively be obtained in conventionalchemical ways.

It is contemplated that NMR measurements of GlycA, Valine, andlipoproteins of a single (blood/plasma) in vitro biosample can provideimportant clinical information and/or further improve a prediction orevaluation of a patient or subject's risk of developing type 2 diabetesand/or having prediabetes.

FIG. 14A illustrates an enlarged chemical shift portion of the NMRspectrum between 2.080 and 1.845 ppm as shown in FIG. 9. FIG. 14A alsoillustrates both the calculated C signal and the measured (composite)signal envelope Cm from the allylic protons of the lipids in VLDL, LDLand HDL, with underlying deconvolved GlycA and GlycB and other resonantpeaks. GlycA can include contributions from 2-NAcGlc and 2-NAcGal methylgroups. GlycB includes signal from the N-acetyl methyl groups on thesialic acid moieties of glycoproteins.

A defined lineshape GlycA mathematical deconvolution model can be usedto measure the GlycA. The “composite” or measured signal envelope Cm canbe deconvolved to quantify the signal contributions of GlycA and othercontributing components such as lipoprotein subclass components. Thedeconvolution calculates signal amplitudes of the componentscontributing to the measured signal shapes and calculates the sum of thecomponents. A close match between the calculated signal C and themeasured signal Cm indicates the deconvolution successfully modeled thecomponents that make up the NMR signal.

The peak region of the GlycA region GA and the peak of the GlycB regionGB are shown by the peaks centered at 2.00 ppm and 2.04 ppm (at about 47deg C. sample temperature), respectively, underlying the composite(upper) envelope signal line Cm. In some embodiments, the peak regionsfor GlycA and GlycB can include adjacent smaller nearby signals in thedeconvolution model to account for GlycA and GlycB signals of slightlydifferent frequency.

The protein signal Ps includes “humps” or peaks P_(GA) and P_(GB) thatalign with GA and GB, respectively. GlycA can be calculated using thedifference between total plasma GlycA signal or “GA” as given by thetotal peak area of the plasma GlycA signal and “P_(GA)”, that portion ofGlycA that may derive from the non-inflammatory glycoproteins in theprotein (d>1.21 g/L) component of plasma. The deconvolution can becarried out to subtract out the (patient/subject) variable “clinicallynon-informative” part of the total NMR signal at the GA region to leavethe more informative disease association measure of GlycA.

Stated differently, while not being bound to any particular theory, insome embodiments, the measured GlycA signal at 2.00 ppm can be referredto as GA, the deconvolution can separate it into three parts: 1) thepart contributed to by the protein (d>1.21 g/L) chosen to be largelydevoid of inflammatory proteins, 2) the part contributed to by the“non-inflammatory” lipoproteins (d<1.21 g/L), and 3) the inflammatoryglycoproteins (both lipoprotein and protein), the latter modeled by theoverlapping Lorentzians (LGA) or other curve fit functions. Theclinically informative GlycA from the deconvolution can be defined as GAminus P_(GA) and minus the non-inflammatory lipoprotein components=LGA.GlycB can be determined in a similar manner using the GB minus P_(GB)signal contribution minus the non-inflammatory lipoprotein components.

The lineshape deconvolution can be achieved with a non-negative leastsquares fitting program (Lawson, C L, Hanson R J, Solving Least SquaresProblems, Englewood Cliffs, N.J., Prentice-Hall, 1974). This avoids theuse of negative concentrations which can lead to error especially in lowsignal to noise spectra. Mathematically, a suitable lineshape analysisis described in detail for lipoproteins in the paper by Otvos, J D,Jeyarajah, E J and Bennett, D W, Clin Chem, 37, 377, 1991. A syntheticbaseline correction function may also be used to account for baselineoffsets from residual protein components. This can take the form of aquadratic or other polynomial function. Weighting factors are determinedand the fit can be optimized by minimizing the root mean squareddeviation between the experimental and calculated spectrum. See, e.g.,U.S. Pat. Nos. 4,933,844 and 6,617,167 for a description of deconvolvingcomposite NMR spectra to measure subclasses of lipoproteins, thecontents of which are hereby incorporated by reference as if recited infull herein. See also, U.S. Pat. No. 7,243,030 for a description of aprotocol to deconvolve chemical constituents with overlapping signalcontribution, the contents of which are hereby incorporated by referenceas if recited in full herein.

FIGS. 14B and 14C illustrate the composite (measured) signal “Cm” of theNMR spectra of FIG. 14A with a fitting region F_(R) corresponding to theNMR spectrum between 2.080 and 1.845 ppm. The fitting region F_(R)typically comprises 315 data points but more or less may be used, suchas between about 200-400 data points, for example. The GlycAquantification/deconvolution model includes VLDL/chylos components, LDLcomponents, and HDL components.

Table 6 shows various TRLs that may be used in an exemplary DRI model.

TABLE 6 Characteristics of Triglyceride Rich Lipoprotein SubclassesMeasured by NMR LipoProfile Analysis TRL Subclass NMR Chemical EstimatedSubclass Co_(m)ponents Shift (ppm) Diameter (nm) Chylomicrons C-2600.8477 260 Chylomicrons C-250 0.8470 250 Chylomicrons C-240 0.8464 240Chylomicrons C-225 0.8457 225 Chylomicrons C-200 0.8443 200 ChylomicronsC-190 0.8440 190 Chylomicrons C-185 0.8436 185 Chylomicrons C-180 0.8429180 Chylomicrons C-175 0.8422 175 Chylomicrons C-170 0.8416 170 TRL V6V6-140 0.8402 140 TRL V6 V6-120 0.8388 120 TRL V6 V6-100 0.8374 100 TRLV5 V5-80 0.8361 80 TRL V5 V5-70 0.8347 70 TRL V5 V5-60 0.8333 60

The term “TRL V6” refers to TRL (triglyceride rich lipoprotein)particles or sub-fractions having a diameter between about 90 nm up toas much as about 170 nm, more typically having diameters between about100-140 nm. The term “TRL V6” can also be defined with respect to thelipid methyl group NMR signal chemical shifts (ppm) corresponding to theestimated diameters as provided in Table I below.

The term “TRL V5” refers to large TRL particles having a diameter ofbetween about 60 nm and about 80 nm (see Table 6 above for theassociated NMR chemical shifts).

The terms “chylomicron” and “chylos” refer to very large TRL particleshaving diameters that are larger than TRL V6. As such chylomicronsreters to TRL particles or sub-fractions having a diameter between fromabout 170 nm up to about 260 nm (see Table 5 above for their associatedNMR chemical shifts). There is not a clear demarcation between TRL V5and TRL V6 nor between TRL V6 and chylomicrons, such that there is adistribution of particle sizes for each subgroup that overlaps in therange between about 80-90 nm for TRL V5-6 and between about 140-170 nmfor TRL V6 & chylomicrons.

When the TRLs are quantified, the concentrations in particleconcentration units (nmol/L) or triglyceride concentration units (mg/dL)can be expressed. Thus, for each of the different definitions of “largeVLDL”, either the particle concentrations or triglyceride concentrationscould be used in the DRI model. Without wishing to be bound to anyparticular theory, based on linear regression analysis, the triglycerideconcentration units may yield marginally better diabetes riskprediction.

FIG. 14D is a table of different components in a GlycA/B deconvolutionmodel according to embodiments of the present invention. Metabolite A isone component that can be measured in a GlycA/B deconvolution model andmay be used clinically. As illustrated in FIGS. 14E and 14F, metaboliteA can be present in a spectrum as a singlet peak and is typicallypresent in a sample at low concentrations (FIG. 14E), but a highconcentration of metabolite A may be present in a sample (FIG. 7F). Aplurality of curve fitting functions for the metabolite A peak regioncan be used to quantitatively evaluate a level of metabolite A and/or todeconvolve the NMR spectrum for quantification of GlycA and/or GlycB,for example.

The deconvolving model components shown in FIG. 14D list a plurality ofcurve fit functions Glyc1-Glyc46 that can be applied to a fitting regionthat includes the GlycA peak region and extends to a GlycB peak region(typically with between about 40-50 curve fit functions, shown as with46, but less or more such curve fit functions may be used, e.g., between30-70). As will be discussed further below, the GlycA measurement can becarried out by summing values of a defined first subset of the curve fitfunctions, values associated with all or some of the Glyc1-Glyc26components, for example. The GlycB measurement can be carried out bysumming values of a second (typically smaller) defined subset of thecurve fit functions, such as some or all components between Glyc27 andGlyc 46, for example.

FIGS. 15A-15D illustrate spectral overlaps from triglyceride richlipoproteins as the TG (triglyceride) values increase which can bechallenging to reliably deconvolve in a manner that provides precise andreliable GlycA and GlycB measurements.

The GlycA/B model provides sufficient HDL, LDL and VLDL/chyloscomponents to be able to provide a good fit of the experimental signalas indicated by a close match between calculated signal C andexperimental or measured composite signal Cm. Typically, the Glyc modelwill have more of the closely spaced VLDL/chylos components than eitherLDL or HDL components as these TRL contribute more signal to the leftside of the spectrum. The model can include 20-50 VLDL/chyloscomponents, typically about 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,36, 37, 38, 39 or 40. In a preferred embodiment, the model includes 30VLDL/chylos components.

The Glyc model can include a plurality “N” of (typically overlapping)curve fit components N that populate a sub-region Fs of the fittingregion F_(R) that extends from a few data points (e.g., about 10 orless) to the right of the GlycA measurement region R₁ (e.g., starting atabout 1.9 ppm or higher) to at least a few data points to the left ofthe GlycB region R₂ (and can extend to the end of the fitting regionF_(R) to 2.080 ppm), at least where GlycB is measured or evaluated. Eachcomponent N, in this embodiment, can be a Lorentzian-shaped signal witha line width about 1.4 Hz. Also, in particular embodiments, each datapoint can be about 0.275 Hz apart as determined by the digitalresolution of the spectrum. The tail portion of the region Fs on theleft side may include more (Lorentzian) components than the tail portionon the right side. The number of components N in the region Fs n can beabout 46 (e.g., about 46 Lorentzians) but more or less components “N”can be used. For example, the region Fs can include, but is not limitedto, between 30-70 Lorentzians, or n=30, 35, 36. 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or60. The curves N are typically Lorentzian functions with line widths athalf-height of between 2-10 data points (0.55-2.75 Hz at 400 MHz), moretypically between 4-6 data points, and are offset from each other by adefined amount, such as, for example, 2 data points (0.55 Hz).

The GlycA and GlycB Lorentzians (or other curve fitting components N)can have the same or different numbers of data points. The GlycBLorentzians N can have the same, less or more data points than the GlycALorentzians N. The Lorentzian fit components “N” can have peak linewidths (LW) of about 1.4 Hz (at half height). However, other LWs can beused including, but not limited to, 1.1, 1.2, 1.3, 1.5 and the like. Tobe clear, data for GlycB can be omitted from the evaluation for the DRIrisk index.

GlycA can be calculated using a defined subset of the number of curvefit components N that fill the entire region R₁. GlycB can be calculatedusing a suitable number of curve fit (e.g., Lorentzian fit) components Nthat fill the entire region R₂. The region R₁ can be between 5-6 Hz. TheGlycB region R₂ can be 7-8 Hz. Optionally, the GlycA components N can beoffset by 2 data points. The GlycB components N can be offset by 4 datapoints.

GlycA can be calculated using a sum of adjacent Lorentzian components N,typically between 9-15, such as 9, 10, 11, 12, 13, 14 and 15 components.GlycB can be the sum of adjacent Lorentzian fit components N, with thesame, more, or less, typically less, than that used for GlycAmeasurements, such as between about 5-10 components N, typically about7, about 8 or about 9 components. The Lorentzians between R₁ and R₂ arenot included in the quantified measurement of either GlycA or GlycB.FIG. 14B illustrates the sum of 7 adjacent Lorentzians used to calculatethe GlycB measurement and the sum of 10 (more narrow) Lorentzians can beused to calculate the GlycA measurements. FIG. 14C illustrates the sumof 9 adjacent Lorentzians used to calculate the GlycB measurement andthe sum of 12 (more closely spaced) Lorentzians can be used to calculatethe GlycA measurements.

The number of HDL, LDL and VLDL components may vary for the GlycAcalculation. As shown, the HDL components can be 20 HDL components(spanning the range of HDL subclass diameters), but more or less can beused, e.g., between about 10-26. As shown, the number of LDL componentsis 9 components (representing different LDL diameters), but more or lesscan be used, e.g., between about 5-20. As shown, the number ofVLDLs/Chylos components is 30, but more or less can be used, e.g., 25-60of different size ranges.

To be clear, while a preferred embodiment describes the curve fitcomponents as Lorentzian fit components, other fitting components may beused including, but not limited to, experimental N-acetyl methyl groupsignals or Gaussian lineshape functions. Thus, any suitable curve fitfunction can be used.

FIG. 16A is a Table of different protein components (Protein 1, Protein2 and Protein 3) that, when used in the Glyc deconvolution model, yieldsdifferent GlycA concentrations and different GlycA associations with CHDevents and All-Cause Death in MESA. FIGS. 16B, 16C and 16D illustratethe respective protein signal Ps in the deconvolved spectrum and thedifferences they exhibit in the amplitudes of the signals in the GlycAand GlycB peak regions. To optimize the calculated GlycA and/or GlycBmeasurement, in some embodiments, the deconvolution model includes adefined protein signal component as discussed above. This protein signalcomponent Ps is for protein other than lipoproteins, e.g., other thanHDL, LDL, VLDL/chylos, e.g., and may be associated with the >1.21 g/Ldensity fraction of plasma obtained by ultracentrifugation (whichincludes albumin and other non-lipoprotein proteins in plasma).

This signal component “Ps” is shown in FIGS. 14A-14C. Surprisingly,although this protein signal Ps does include a peak (P_(GA), P_(GB),respectively) aligned with the peak at the chemical shift for both GlycAand GlycB, eliminating this portion of the protein NMR signal from thedeconvolution model (by, for example, digital manipulation or signalprocessing) was found to make the calculated GlycA and GlycBmeasurements relatively less clinically informative (weaker diseaseassociations). At the other extreme, including in the deconvolutionmodel a protein component with a relatively large signal at the GlycAand GlycB positions results in lower GlycA and GlycB concentrations thatare also less clinically informative, as shown for Protein #2 andProtein #3 in FIGS. 16C and 16D. Thus, by selecting an appropriateprotein component with an intermediate signal amplitude at the GlycA andGlycB positions, such as Protein #1 in FIG. 16B, the deconvolution modelmay be “tuned” to produce GlycA and GlycB concentrations that areimproved and/or optimized with respect to their clinical associationswith inflammation and related disease states.

Thus, in some embodiments, it is contemplated that the GlycA measurementwill provide a better clinical indicator if it does not include thelipoprotein signal (accounted for in the deconvolution model with theVLDL/chylo, LDL and HDL components) and if it includes only a portion ofthe remaining NMR signal, e.g., it does not include all other NMRprotein signal at the GlycA peak region. This subset of the NMR signalat the GlycA peak region may be more reflective of inflammatory proteinactivity, e.g., N-acetyl methyl signals from glycosylated acute phaseproteins.

FIG. 17 is a screen shot of the deconvolution of a 10 mmol/L referencestandard sample of N-acetylglucosamine, from which a conversion factorof 17.8 was determined to transform signal area concentrations of GlycAand GlycB to μmol/L glycoprotein N-acetyl methyl group concentrations.In some embodiments, according to MESA subjects, first to fourthquartile (mean) levels of GlycA can be: Q1: 21.6*17.8, Q2: 25.8*17.8,Q3: 29.3*17.8 and Q4: 35.3*17.8.

GlycA measurement precision using the model shown in FIG. 14B was shownto be good. A within-run (5 pools from 2009) analysis of lowestGlycA=40.5 (CV=2.47%) and highest GlycA=58.4 (CV=1.6%). Within-labresults from 13 pools from 2010 and 2011 had a lowest GlycA=25.6(CV=4.08%) and highest GlycA=69.1 (CV=1.87%). These concentrations areexpressed as “arbitrary units” of NMR signal areas and can be multipliedby 17.8 to convert them to μmol/L N-acetyl methyl group concentrations.

It is believed that the measured amplitude of the GlycA signal in anyone sample may have the advantage of providing a more stable and“time-integrated” measure of the patient's inflammation state than isprovided by measurements of hs-CRP or other individual inflammatoryproteins.

As noted above, FIG. 17 illustrates a conversion factor that can be usedto calculate measurements of GlycA. The GlycA measurement can also be aunit less parameter as assessed by NMR by calculating an area under apeak region at a defined peak in NMR spectra. In any event, measures ofGlycA with respect to a known population (such as MESA) can be used todefine the level or risk for certain subgroups, e.g., those havingvalues within the upper half of a defined range, including values in thethird and fourth quartiles, or the upper 3-5 quintiles and the like.

FIGS. 18A-18C are exemplary flow diagrams of operations that can be usedto obtain NMR signal associated with Valine according to embodiments ofthe present invention.

FIG. 18A illustrates that a pre-analytical evaluation (block 710) canoccur before a Valine region of the NMR signal is determined (block725), then deconvolved (block 750). FIG. 18B illustrates an exemplarypre-analytical evaluation 810 which includes delivery verification ofthe sample into the flow cell as either complete failure (block 812) orpartial injection failure (block 813), shimming verification (block815), temperature verification (block 817) and a citrate tube detection(failure) (block 819), all using defined characteristics of signalassociated with a defined diluent added to the sample.

Referring again to FIG. 18A, once the defined parameters are confirmedwithin limits, the pre-analytical quality control analysis can end(block 720) and the determination of the Valine region can be identified(block 725) and the spectrum deconvolved and Valine level calculated(block 750). Optionally, a post-analytical quality control can beelectronically performed (block 755) and the results output (block 760).The results can be included in a test report with comments, visualindicia of high or low and the like (block 765).

Referring to FIG. 18C, NMR signal can be electronically obtained of anin vitro biosample with a defined added diluent (block 902). The QCevaluation can be carried out (block 910). The Valine region isdetermined (block 925). The Valine region is deconvolved (block 950 d)and an NMR derived value of Valine is calculated (950 c).

The diluent can comprise calcium ethylenediamine tetraacetic acid (CaEDta) (block 903) or other suitable diluent that creates a reliable peakand behaves in a predictable manner. Well established chemical shift orquantitation references include, for example, formate,trimethylsilylpropiolate (and isotopically labeled isomers), and EDTA.

The pre-analytical quality control evaluation 810 can be based oninspection of characteristics of the CaEDTA reference peak and thesystem or processor can be configured not to perform the Valine testunless the NMR spectra have been acquired under specified conditionssuch as those shown in FIG. 18B. The sample temperature can be 47±0.5°C. in the flow cell for NMR scans/signal acquisition. The sample cancomprise diluents in a 1:1 ratio (block 905) or other defined ratio(e.g., more sample, less diluents or more diluent; less sample, e.g.,2:1 or more sample, less diluents, e.g. 1:2).

The test sample can be rejected with a defined error code if CaEDTAheight >140 for any acquired spectrum (block 919). This high value isindicative of detection of the citrate peak in conjunction with theCaEDTA peak. The citrate peak is introduced by collection of thespecimen in an improper citrate tube. By disrupting the ability tolocate the exact position of the CaEDTA peak, the citrate peak candisrupt the process for determining the Valine region.

The Valine region is located upfield relative to the position of theCaEDTA peak. The broad peaks beneath Valine are various methyl (—CH₃—)protons of lipoproteins. The CaEDTA location can be determined atapproximately 22258±398 data points (block 921). The Valine region canbe determined independently for each acquired spectrum. The Valinesignal can be modeled with suitable data points using, for example, 25data points (center±12 data points) for each peak of the quartet or 300data points for the Valine region of both doublets, but other numbers ofdata points may be used. The measurement of Valine can be carried outusing one, two, three or all four peaks of the Valine peak quartet.

All basis set spectra can be linearly interpolated before utilized bythe non-negative least squares algorithm. The spectra to be analyzed andthe basis set spectra can have a zero baseline offset modificationbefore utilized by the non-negative least squares algorithm.

The start of the Valine region can be at about 2196-4355 data points,typically the latter when including both doublets, (the “Valine regionoffset”) upfield from the location of the CaEDTA peak (block 922). Insome embodiments, the start of the Valine region is at 4355 data pointsupfield from the location of the CaEDTA peak.

In some embodiments, the Valine quantification is carried out bycharacterizing the Valine resonances at between 0.0-1.01 ppm as twodoublets. Three or more Valine experimental spectra stepped by two datapoints can be used as basis sets to model Valine signal. The centerValine peaks can be located by sliding three Valine components +/− 15data points and determined through a least squares sum minimization. TheValine signal can be modeled with a total of about 300 data points.

Each basis set, including those used for the baseline but excluding theDC offset, can be offset such that the lowest value is subtracted fromthe function (making the lowest point equal to 0). This preventsinclusion of a DC offset in the shapes they represent.

The Valine region from each acquired spectrum is deconvolved with aseries of analyte and baseline functions which have been treated to thesame type of pre-processing as the acquired spectra. Shim status of theNMR spectrometer during the signal AT for Valine or other BCAA can bemonitored by concurrently evaluating linewidth of a reference peak. Adefined adjustment factor based on the linewidth of the reference peakcan be applied to the calculated Valine concentration.

The deconvolution coefficient for each component can be multiplied by anassociated conversion factor. The current Valine embodiment has aconversion factor of 2271 to report Valine in μM units; however, thisvalue can vary by ±10% without unduly affecting the reported valuesignificantly. Other conversion factors may be used in the future.

Basis Function Starting Component Conversion position relative to NameFilename Factor CaEDTA Valinel Valine318LB019.1r 2271 −4353 Valine2Valine318LB019.1r 2271 −4355 Valine3 Valine318LB019.1r 2271 −4357

The resulting values are summed. Result values produced independentlyfor each acquired spectrum can be averaged to generate final values touse in the measurement.

Data can be acquired using presaturation water suppression from a 1:1diluted sample and can include between 5-20 scans, typically about 10scans stored as 5 blocks of 2 (5 FIDs consisting of 2 scans each) (block926).

The pulse sequence used in conjunction with presaturation watersuppression can optionally include a presaturation (water suppression)pulse and a suitable excitation pulse. FIDs can be acquired with 9024data points with a sweep width of 4496.4 Hz. Each FID can be multipliedwith a shifted Gaussian function:

$e^{- {(\frac{({t - {gfs}})}{gf})}^{2}},$or in computer terms, exp(−((t−gfs)/gf)^2), where gfs=0.2 seconds andgf=0.2 seconds.

This can be performed prior to Fourier transformation with zero-fillingwhich yields the frequency-domain GM spectrum for each FID consisting of16,384 data points (block 927). The spectra can be phased using thecalibration-specified phase value. The spectra can be scaled(multiplied) by a calibration-specified scaling factor. All basis setspectra can be linearly interpolated before utilized by the non-negativeleast squares algorithm. The spectra to be analyzed and the basis setspectra can have a zero baseline offset modification before utilized bythe non-negative least squares algorithm (e.g., all components used forthe model and the spectrum that will be analyzed can be linearlyinterpolated) (block 928). To determine the center of the Valine fittingregion, the Valine resonances between 0.9 and 1.0 as two doublets can becharacterized and the center peaks can be identified by sliding threeValine components±15 data points (block 929).

FIG. 19 is a chart of prospective associations of hs-CRP and NMRmeasured GlycA and Valine levels with various exemplary disease outcomesbased on MESA data (n≈5680). The chart was generated from logisticregression analyses adjusted for age, gender, race, smoking, systolicblood pressure, hypertension medications, body mass index, diabetes,LDL-P and HDL-P. The likelihood ratio statistic χ2 gives a quantitativemeasure of the extent to which the indicated variable improves diseaseprediction when added to the 10 covariates in the regression model. Theanalyses used GlycA measurement values from the deconvolution modelshown in FIG. 7B. The right side column shows that GlycA and Valine areadditive in their associations with disease when they both havesignificant associations examined separately.

FIG. 20 is a Table of Characteristics of MESA subjects by NMR measuredGlycA quartile. The mean GlycA level of those in the 3rd quartile is29.3. This table shows that people with higher GlycA levels havecharacteristics associated with higher inflammation (more smoking,hypertension, hs-CRP, etc). NMR signal area units can be called“arbitrary” units. The GlycA levels in this table are in these“arbitrary units” that may be converted to methyl group concentrationunits (umol/L) by multiplying by 17.8.

Referring now to FIG. 21, it is contemplated that most, if not all, themeasurements can be carried out on or using a system 10 in communicationwith or at least partially onboard an NMR clinical analyzer 22 asdescribed, for example, with respect to FIG. 22 below and/or in U.S.Pat. No. 8,013,602, the contents of which are hereby incorporated byreference as if recited in full herein.

The system 10 can include a Diabetes Risk Index Module 370 to collectdata suitable for determining the DRI (e.g., HDL subpopulations, GlycA,Valine). The system 10 can include an analysis circuit 20 that includesat least one processor 20 p that can be onboard the analyzer 22 or atleast partially remote from the analyzer 22. If the latter, the Module370 and/or circuit 20 can reside totally or partially on a server 150.The server 150 can be provided using cloud computing which includes theprovision of computational resources on demand via a computer network.The resources can be embodied as various infrastructure services (e.g.computer, storage, etc.) as well as applications, databases, fileservices, email, etc. In the traditional model of computing, both dataand software are typically fully contained on the user's computer; incloud computing, the user's computer may contain little software or data(perhaps an operating system and/or web browser), and may serve aslittle more than a display terminal for processes occurring on a networkof external computers. A cloud computing service (or an aggregation ofmultiple cloud resources) may be generally referred to as the “Cloud”.Cloud storage may include a model of networked computer data storagewhere data is stored on multiple virtual servers, rather than beinghosted on one or more dedicated servers. Data transfer can be encryptedand can be done via the Internet using any appropriate firewalls tocomply with industry or regulatory standards such as HIPAA. The term“HIPAA” refers to the United States laws defined by the Health InsurancePortability and Accountability Act. The patient data can include anaccession number or identifier, gender, age and test data.

The results of the analysis can be transmitted via a computer network,such as the Internet, via email or the like to a patient, clinician site50, to a health insurance agency 52 or a pharmacy 51. The results can besent directly from the analysis site or may be sent indirectly. Theresults may be printed out and sent via conventional mail. Thisinformation can also be transmitted to pharmacies and/or medicalinsurance companies, or even patients that monitor for prescriptions ordrug use that may result in an increase risk of an adverse event or toplace a medical alert to prevent prescription of a contradictedpharmaceutical agent. The results can be sent to a patient via email toa “home” computer or to a pervasive computing device such as a smartphone or notepad and the like. The results can be as an email attachmentof the overall report or as a text message alert, for example.

Still referring to FIG. 21, one or more electronic devices 50D, 51D, 60Dassociated with the different users, e.g., a clinician site 50, patient52D and/or a test or lab site 60 can be configured to access anelectronic analysis circuit 155 in communication with a display of arespective electronic device. The analysis circuit 155 can be hosted ona server and can provide an Internet portal or downloadable APP or othercomputer program for various devices. The circuit 155 can configured toallow a user, e.g., a clinician to enter one or more of: (i) a glucosevalue of a patient, (ii) a glucose value of a patient and a diabetesrisk index score, or (iii) a diabetes risk index score. The circuit canautomatically populate different data fields based on a patientidentifier or other password at sign-in or allow a user to enter boththe DRI score and the glucose measurement for a respective patient. Theanalysis circuit can be configured to track changes in the DRI scoreover time and generate electronic reports that can be sent toclinicians, patients or other users. The analysis circuit can also sendnotices for recommendations on retests, follow-up tests and the like,e.g., if a DRI risk score is elevated or above a low risk value, e.g.,in an intermediate risk category, the circuit can notify the clinicianthat a glucose test may be appropriate or send a notice to the patientto confer with the doctor to see if a glucose test is appropriate orwhether increased monitoring intervals for follow-on DRI tests may bedesirable. The analysis circuit can generate a risk progression pathwayor analysis to provide graphic information that stratifies risk ofdeveloping type 2 diabetes in the future for patients having the sameglucose value when the glucose value is in an intermediate risk range,when fasting plasma glucose levels are between 90-110 mg/dL, A1C %levels are between 5.7-6.4 or oral glucose tolerance levels are between140-199 mg/dL. The electronic analysis circuit can be onboard the server150 in the Cloud or otherwise accessible via the Internet 227 or can beassociated with a different client architecture as will be appreciatedby one of skill in the art. Thus, a clinician, patient or other user cangenerate a customized report on risk progression or otherwise obtainrisk stratification information.

Referring now to FIG. 22, a system 207 for acquiring at least one NMRspectrum for a respective biosample is illustrated. The system 207includes an NMR spectrometer 22 s and/or analyzer 22 for obtaining NMRdata for NMR measurements of a sample. In one embodiment, thespectrometer 22 s is configured so that the NMR signal acquisition isconducted at about 400 MHz for proton signals; in other embodiments themeasurements may be carried out at between about 200 MHz to about 900MHz or other suitable frequency. Other frequencies corresponding to adesired operational magnetic field strength may also be employed.Typically, a proton flow probe is installed, as is a temperaturecontroller to maintain the sample temperature at 47+/−0.5 degrees C. Thespectrometer 22 is controlled by a digital computer 214 or other signalprocessing unit. The computer 211 should be capable of performing rapidFourier transformations. It may also include a data link 212 to anotherprocessor or computer 213, and a direct-memory-access channel 214 whichcan connects to a hard memory storage unit 215 and/or remote server 150(FIG. 15).

The digital computer 211 may also include a set of analog-to-digitalconverters, digital-to-analog converters and slow device I/O ports whichconnect through a pulse control and interface circuit 216 to theoperating elements of the spectrometer. These elements include an RFtransmitter 217 which produces an RF excitation pulse of the duration,frequency and magnitude directed by the digital computer 211, and an RFpower amplifier 218 which amplifies the pulse and couples it to the RFtransmit coil 219 that surrounds sample cell 220 and/or flow probe 220p. The NMR signal produced by the excited sample in the presence of a9.4 Tesla polarizing magnetic field produced by superconducting magnet221 is received by a coil 222 and applied to an RF receiver 223. Theamplified and filtered NMR signal is demodulated at 224 and theresulting quadrature signals are applied to the interface circuit 216where they are digitized and input through the digital computer 211. TheDRI risk evaluation Module 370 or analysis circuit 20 (FIGS. 21, 22) ormodule 350 (FIG. 23) or can be located in one or more processorsassociated with the digital computer 211 and/or in a secondary computer213 or other computers that may be on-site or remote, accessible via aworldwide network such as the Internet 227.

After the NMR data are acquired from the sample in the measurement cell220, processing by the computer 211 produces another file that can, asdesired, be stored in the storage 215. This second file is a digitalrepresentation of the chemical shift spectrum and it is subsequentlyread out to the computer 213 for storage in its storage 225 or adatabase associated with one or more servers. Under the direction of aprogram stored in its memory, the computer 213, which may be a personal,laptop, desktop, workstation, notepad, tablet or other computer,processes the chemical shift spectrum in accordance with the teachingsof the present invention to generate a report which may be output to aprinter 226 or electronically stored and relayed to a desired emailaddress or URL. Those skilled in this art will recognize that otheroutput devices, such as a computer display screen, notepad, smart phoneand the like, may also be employed for the display of results.

It should be apparent to those skilled in the art that the functionsperformed by the computer 213 and its separate storage 225 may also beincorporated into the functions performed by the spectrometer's digitalcomputer 211. In such case, the printer 226 may be connected directly tothe digital computer 211. Other interfaces and output devices may alsobe employed, as are well-known to those skilled in this art.

Embodiments of the present invention may take the form of an entirelysoftware embodiment or an embodiment combining software and hardwareaspects, all generally referred to herein as a “circuit” or “module.”

As will be appreciated by one of skill in the art, the present inventionmay be embodied as an apparatus, a method, data or signal processingsystem, or computer program product. Accordingly, the present inventionmay take the form of an entirely software embodiment, or an embodimentcombining software and hardware aspects. Furthermore, certainembodiments of the present invention may take the form of a computerprogram product on a computer-usable storage medium havingcomputer-usable program code means embodied in the medium. Any suitablecomputer readable medium may be utilized including hard disks, CD-ROMs,optical storage devices, or magnetic storage devices.

The computer-usable or computer-readable medium may be, but is notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, device, or propagation medium. Morespecific examples (a non-exhaustive list) of the computer-readablemedium would include the following: an electrical connection having oneor more wires, a portable computer diskette, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, and a portable compactdisc read-only memory (CD-ROM). Note that the computer-usable orcomputer-readable medium could even be paper or another suitable medium,upon which the program is printed, as the program can be electronicallycaptured, via, for instance, optical scanning of the paper or othermedium, then compiled, interpreted or otherwise processed in a suitablemanner if necessary, and then stored in a computer memory.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java7, Smalltalk, Python, Labview, C++, or VisualBasic. However, thecomputer program code for carrying out operations of the presentinvention may also be written in conventional procedural programminglanguages, such as the “C” programming language or even assemblylanguage. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network (LAN) or awide area network (WAN) or secured area network (SAN), or the connectionmay be made to an external computer (for example, through the Internetusing an Internet Service Provider).

The flowcharts and block diagrams of certain of the figures hereinillustrate the architecture, functionality, and operation of possibleimplementations of analysis models and evaluation systems and/orprograms according to the present invention. In this regard, each blockin the flow charts or block diagrams represents a module, segment,operation, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that in some alternative implementations, thefunctions noted in the blocks might occur out of the order noted in thefigures. For example, two blocks shown in succession may in fact beexecuted substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved.

FIG. 23 is a block diagram of exemplary embodiments of data processingsystems 305 that illustrates systems, methods, and computer programproducts in accordance with embodiments of the present invention. Theprocessor 310 communicates with the memory 314 via an address/data bus348. The processor 310 can be any commercially available or custommicroprocessor. The memory 314 is representative of the overallhierarchy of memory devices containing the software and data used toimplement the functionality of the data processing system 305. Thememory 314 can include, but is not limited to, the following types ofdevices: cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, and DRAM.

As shown in FIG. 23, the memory 314 may include several categories ofsoftware and data used in the data processing system 305: the operatingsystem 352; the application programs 354; the input/output (I/O) devicedrivers 358; a DRI module 370 and the data 356. The Diabetes Risk IndexEvaluation Module 370 can consider the level of the measured GlycA,lipoprotein components and optionally also Valine and also optionally,glucose, in a multi-parameter mathematical model of risk of developingtype 2 diabetes in the future, e.g., the next 5-7 years, and/or alikelihood of having prediabetes.

The data 356 may include signal (constituent and/or composite spectrumlineshape) data 362 which may be obtained from a data or signalacquisition system 320. As will be appreciated by those of skill in theart, the operating system 352 may be any operating system suitable foruse with a data processing system, such as OS/2, AIX or OS/390 fromInternational Business Machines Corporation, Armonk, N.Y., WindowsCE,WindowsNT, Windows95, Windows98, Windows2000 or WindowsXP from MicrosoftCorporation, Redmond, Wash., PalmOS from Palm, Inc., MacOS from AppleComputer, UNIX, FreeBSD, or Linux, proprietary operating systems ordedicated operating systems, for example, for embedded data processingsystems.

The I/O device drivers 358 typically include software routines accessedthrough the operating system 352 by the application programs 354 tocommunicate with devices such as I/O data port(s), data storage 356 andcertain memory 314 components and/or the NMR spectrometer or analyzer22. The application programs 354 are illustrative of the programs thatimplement the various features of the data processing system 305 and caninclude at least one application, which supports operations according toembodiments of the present invention. Finally, the data 356 representsthe static and dynamic data used by the application programs 354, theoperating system 352, the I/O device drivers 358, and other softwareprograms that may reside in the memory 314.

While the present invention is illustrated, for example, with referenceto the Modules 350, 370 being an application program in FIG. 23, as willbe appreciated by those of skill in the art, other configurations mayalso be utilized while still benefiting from the teachings of thepresent invention. For example, the GlycA Module 350 and/or the DRIModule 370 may also be incorporated into the operating system 352, theI/O device drivers 358 or other such logical division of the dataprocessing system 305. Thus, the present invention should not beconstrued as limited to the configuration of FIG. 23, which is intendedto encompass any configuration capable of carrying out the operationsdescribed herein.

FIG. 24 is a flow chart of exemplary operations that can carry outembodiments of the present invention. A (measured) composite envelopeNMR spectrum of NMR spectra of a fitting region of a biosample (e.g.,blood plasma or serum) can be obtained (block 500). The NMR compositesignal envelope is electronically deconvolved using a defined modelhaving HDL, LDL and VLDL/Chylos components and a plurality of curve fit(e.g., Lorentzian) functions associated with at least a GlycA peakregion centered at a defined chemical shift location (e.g., 2.00 ppm)associated with GlycA (block 502). A defined number of curve fittingfunctions for the peak region associated with GlycA can be summed (block515). A conversion factor can be applied to the summed functions togenerate a calculated measurement of GlycA (block 520).

The method can include providing at least one defined mathematical modelof risk of progression to type 2 diabetes (e.g., within 5-7 years) thatis used to calculate a diabetes risk score (block 523). The method caninclude identifying whether the patient is at risk of developing type 2diabetes and/or has prediabetes based on the defined mathematical riskmodel that includes a plurality of lipoprotein components and GlycA orValine to generate the DRI score (block 524).

Optionally, the DRI and/or GlycA calculations can be provided in apatient and/or clinical trial report (block 522).

The defined GlycA deconvolution model can include a protein signalcomponent at a density greater than about 1.21 g/L that can bedeconvolved/separated from the signal composite envelope (block 503).

FIG. 25A is a schematic illustration of an exemplary patient test report100 that can include various lipoprotein parameters such as two or moreof DRI, GlycA, Valine, HDL-P, LDL-P 101. The DRI and/or GlycA number 101can be presented with risk assessment summary 101s correlated topopulation norms, graphs, typical ranges, and/or degree of risk (e.g.,high, increased or low risk), shown as a sliding scale graph in FIG.25A.

However, other risk summary configurations may be used including ranges,high to low or low to high, or just noting whether the associated riskis low, medium or increased and/or high.

FIG. 25B is another example of a patient report with a visual (typicallycolor-coded) graphic summary of a continuum of risk from low to highaccording to embodiments of the present invention.

FIG. 26 illustrates that a graph 130 of DRI values over time can beprovided to illustrate a change in patient health and/or inflammatorystatus over time due to age, medical intervention or a therapy accordingto some embodiments. Tracking this parameter may provide a clinicalindicator of efficacy of a therapy and/or a better risk predictor fortype 2 diabetes for patients. As shown in FIG. 26, the graph analysiscan be used to monitor a patient over time to correlate known start oruse of a drug or other therapy. Future drugs or uses of known drugs canbe identified, screened or tested in patients identified using DRIand/or GlycA evaluations of drugs or therapies of any suitable diseasestate including, for example, anti-diabetes and anti-obesity therapies.

The tracking can be provided via a tracking module that can be providedas an APPLICATION (“APP”) for a smartphone or other electronic formatfor ease in tracking and/or for facilitating patient compliance with atherapy. Similarly, an APP for clinicians and/or patients can beprovided that allows input of glucose measurement when known along witha DRI score to generate a risk trajectory for evaluating risk andproviding an easy to understand risk stratification comparison for aclinician or patient. Passwords or other security measures that complywith privacy and/or HIPPA guidelines can be used with such an APP.

FIGS. 27A and 27B are graphical patient/clinical reports of % risk ofdiabetes versus FPG level and DRI score and risk pathway. FIG. 27A showspatient #1's score while FIG. 27B shows patient #1's score in comparisonwith a lesser risk patient (patient number 2) having the same FPG. Whileeach patient has the same FPG, they have different metabolic issuesidentified by the DRI scores stratifying risk according to embodimentsof the present invention.

FIGS. 28A-28C are graphical patient/clinical reports of diabetesconversion rate (%) versus FPG level and DRI score (high DRI, Q4 and lowDRI, Q1) according to embodiments of the present invention. FIG. 28A isfor a 4-year risk of conversion to diabetes. FIG. 28B is a 5-year riskof conversion and FIG. 28C is a 6 year risk of conversion.

FIG. 29 is a graphical patient/clinical report of a Q4/Q1 relative riskof diabetes conversion (1-8) versus FPG level and DRI score for both6-year (upper line) and 2-year conversions periods according toembodiments of the present invention.

FIG. 30 is a graphical patient/clinical report of log scale 5 yearconversion with a diabetes conversion rate (%) versus FPG level and DRIscore (high DRI, Q4 and low DRI, Q1) color coded from green, yellow,pink/orange to red and with a legend textually correlating the risk asvery high, high, moderate and low, according to embodiments of thepresent invention.

FIG. 31 is a graphical patient/clinical report of 5 year conversion todiabetes with a diabetes conversion rate (%) versus FPG level and DRIscore (high DRI, Q4 and low DRI, Q1) color coded from green, yellow,pink/orange to red and with a legend textually correlating the risk asvery high, high, moderate and low, according to embodiments of thepresent invention.

Further embodiments of the invention will now be described by way of thefollowing non-limiting Examples.

EXAMPLES Example 1

A Diabetes Risk index (DRI) was developed using MESA that uses onlyinformation derived from a single nuclear magnetic resonance (NMR)spectrum of a fasting plasma sample. The DRI can identify thehighest-risk patients who are likely to benefit the most fromintervention. This information includes glucose and lipoproteinsubclass/size parameters previously linked to insulin resistance, aswell as Valine, and GlycA. FIG. 1A was generated using NMR spectracollected at baseline from the Multi-Ethnic Study of Atherosclerosis(MESA). The MESA dataset consisted of 3185 participants, 280 of whomdeveloped diabetes during 5 years of follow-up. FIG. 1A presents thediabetes conversion rates of subjects within 6 glucose subgroups (dottedline), and those for subjects in the upper and lower quartile of DRIwithin each glucose stratum. As shown, the risk of developing diabetesat any given glucose level is substantially greater for DRI in Q4 vs Q1.The NMR-based diabetes risk score can effectively stratify risk withoutthe need for additional clinical information.

In this analysis, predictive modeling techniques were used to identify a“best” logistic regression model of five year diabetes conversion. Themodeling used clinical data from the MESA study as well as NMR derivedlipid and metabolite data. Although the final model selection restrictedthe data to subjects with baseline glucose less than or equal to 115,initial modeling considered the possibility of different “best” modelsfor subjects with baseline glucose less than or equal to 100, subjectswith baseline glucose greater than 100 but less than or equal to 115,and subjects with baseline glucose greater than 115 but less than orequal to 125.

One selected model for predicted five year conversion to diabetesincluded baseline glucose (glucos1c), VLDL size (vsz3), the ratio ofmedium HDL-P to total HDL-P (HMP_HDLP), medium HDL-P (hmp3), the sum oflarge VLDL-P and medium VLDL-P (vlmp3), GlycA, and Valine. This modelincluded two interactions: HMP_HDLP by GlycA and vsz3 by vlmp3. It wasbuilt using data from subjects with baseline glucose less than or equalto 115.

During development of these parameters, large VLDL-P (vlp3) was replacedby vlmp3 (sum of vmp3 and vlp3) in the NMR model as it was found to givebetter predictive results. Further exploration showed that the NMR modelcould include an interaction between VLDL size and vlmp3.

The final series of analyses showed that VLDL size and vlp3 (prior tocalculation of vlmp3) could be appropriately truncated at low and highvalues without degrading the predictive accuracy. The benefit of suchtruncation may be model robustness. Also, the analysis showed thataccounting for possible non-linear effects of VLDL size and vlmp3 onfive year conversion was unnecessary.

Example 2

The DRI index model can employ lipoproteins, Valine and GlycA as sevendifferent parameters (including 5 lipoprotein parameters): VLDL size,large+medium VLDL particle number, total HDL and medium HDL subclassparticle number, Valine, and GlycA.

Another study of the MESA dataset consisted of 4985 non-diabeticparticipants, 411 of whom developed diabetes during 6 years offollow-up. The MESA data restricted to the 1832 individuals withintermediate glucose 90-110 mg/dL, 198 of whom converted to diabetes.Conversion rates by quintile of a baseline DRI index and the fourcomponent parts of the DRI model: lipoproteins, GlycA, Valine, andglucose were assessed. Relative rates for those in the extreme quintileswere 2.2 for lipoproteins, 1.9 for GlycA, 1.7 for Valine, 6.3 forglucose, and 10.7 for DRI (2.2% in Q1; 23.0% in Q5).

The results indicate that the DRI score, without any additional clinicalinformation, can identify among patients with intermediate glucoselevels those with diabetes risk differing >10-fold. Ratios between thefirst quintile and the fifth quintile establish that there is a 10.7ratio for DRI which indicates that patients can have a 10 folddifference in diabetes risk when the FPG is in the range of 90-110mg/dL.

It is believed that the new DRI scores can allow at-risk patients to betargeted for intervention before the onset of substantial beta-celldysfunction.

Example 3 MESA and IRAS Comparison

FIG. 32 is a table of data that shows the performance of DRI (withglucose) in the IRAS dataset, the MESA dataset, and IRAS dataset (usingthe glucose subgroups from MESA). When IRAS samples were collected, thedefinitions of pre-diabetes and diabetes were different than that ofMESA, which occurred years later.

FIG. 33 shows the performance of DRI (without glucose) in the samedataset criteria as FIG. 32. The highlighted values show the differencebetween the 5th quintile and the first quintile.

IRAS:

Observational study of middle-aged Hispanic, non-Hispanic white, andAfrican-American men and women. Blood samples obtained 1992-1994. NMRanalyses of thawed-refrozen heparin plasma performed in 2001 on Varianinstruments (preceding Profilers or current generation NMR analyzersused by LipoScience, Inc., Raleigh, N.C.) using WET water suppression.NMR dataset population was 46% normoglycemic (old definition, glucose<110 mg/dL), 22% impaired glucose tolerance, 32% diabetic. Theseanalyses are for the n=982 nondiabetic subjects, 134 of whom developeddiabetes during a mean 5.2 years of follow-up.

Spearman Correlation in MESA and IRAS Logistic Regression Results:Conversion to New Diabetes in IRAS and MESA

IRAS, 134 New Diab MESA, 411 Newdiab (N = 961) (N = 4985) parametersadjusted on glucose age gender race model param model param X² X² ParamP X² X² Param P LPIR 120.8 27.4 <0.0001 776.8 27.5 <0.0001 DRInmr (no108.0 16.7 <0.0001 796.2 47.9 <0.0001 glucose) DRI (LPIR) no 111.6 19.4<0.0001 796.1 46.8 <0.0001 glucose Vsz 96.8 7.9 0.005 750.1 18.8 <0.0001Lsz 101.4 9.9 0.0017 760.0 11.5 0.0007 Hsz 112.8 18.5 <0.0001 760.2 11.10.0009 Vlp 106.3 16.4 <0.0001 752.1 3.7 0.0559 Lsp 107.5 17.1 <0.0001757.1 8.7 0.0032 hlp 108.3 15.1 0.0001 756.0 7.0 0.0082 GlycA 93.2 2.3755.8 7.5 0.0063 Valine 93.7 2.7 0.099 767.5 19.1 <0.0001 lsp lsz hszhlp vlp 120.6 HSZ 767.0 VLP vsz VSZ

Example 4

The model parameters below were derived from regression models thatincluded glucose/size/metabolite/ratio/SizePlus family. The initial formof these models included baseline glucose, VLDL size (vsz3), GlycA,medium HDL-P ratio (HMP_HDLP), medium HDL-P (hmp3), large VLDL-P (vlp3),and the interaction between GlycA and medium HDL-P ratio. Additionalvariable investigation determined that gender and Valine could be addedto this model, but the addition of alanine did not improve predictiveaccuracy. Further study and discussion determined that large VLDL-P(vlp3) should be replaced by the sum of large and medium VLDL-P (vlmp3).Also, exploratory analyses determined that an interaction between VLDLsize (vsz3) and vlmp3 was statistically significant in the model. Themodeling used subjects in the training dataset with baseline glucoseless than or equal to 115 for model training, and subjects in the testdataset with baseline glucose less than or equal to 115 for modeltesting.

DRI models can be based on a likelihood and/or predicted five yearconversion to diabetes. The risk evaluation models can include VLDL size(vsz3), a ratio of medium HDL-P to total HDL-P (HMP_HDLP), medium HDL-P(hmp3), a sum of large VLDL-P and medium VLDL-P (vlmp3), GlycA, andValine.

This model includes two interactions: HMP_HDLP by GlycA and vsz3 byvlmp3. The model can optionally also include a baseline glucose(glucos1c).

Additional modeling determined that VLDL size (vsz3) and large VLDL-P(vlp3, prior to calculation of vlmp3) could be truncated withoutdegradation to predictive accuracy. For vsz3, any value less than 39.2was truncated to 39.2, and any value greater than 65.1 was truncated to65.1. For vlp3, any value less than 0.7 was truncated to 0.7, and anyvalue greater than 7.9 was truncated to 7.9.

DRIn Non LPIR Model C-statistic 0.840

Standard Wald Pr > Parameter DF Estimate Error Chi-Square ChiSqIntercept 1 −17.6413 1.6351 116.4032 <.0001 Glucose 1    0.1236 0.00865204.2648 <.0001 vsz 1    0.0928 0.0204 20.7076 <.0001 GlycA 1  −0.04090.0361 1.2822 0.2575 HMP_HDLP 1  −7.9057 2.7708 8.1410 0.0043 GlycA* 1   0.3066 0.0884 12.0228 0.0005 HMP_HDLP Hmp 1  −0.0790 0.0325 5.92060.0150 VLMP 1    0.0608 0.0268 5.1522 0.0232 vsz*VLMP 1  −0.001400.000535 6.8493 0.0089 Valine 1    0.00856 0.00310 7.6069 0.0058DRI-LPIR Model C-Statistic 0.839

Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSqIntercept 1 −14.8629 1.2903 132.6852 <.0001 Valine 1    0.00756 0.003125.8947 0.0152 Glucose 1    0.1235 0.00867 203.0915 <.0001 LPIR 1   0.0253 0.00626 16.2565 <.0001 hmp 1  −0.2651 0.0651 16.5696 <.0001vmp 1    0.0165 0.0110 2.2379 0.1347 LPIR*vmp 1  −0.00051 0.0001946.7620 0.0093 GlycA 1  −0.0170 0.0304 0.3123 0.5762 hmp*GlycA 1   0.00738 0.00209 12.4268 0.0004

Example 5

The Diabetes Risk Index (DRI) test can be a lab-based multivariate assaythat employs a defined mathematical model to yield a single compositescore of one's risk of developing type II diabetes in 5 years.Predictive biomarkers included in the assay include: fasting glucose,lipoprotein sub-fractions, branched chain amino acid(s) and one or moreinflammatory biomarker(s). Clinical performance is believed to besuperior to fasting glucose alone in individuals with FPG<125, and iscontinually more predictive of diabetes risk vs. FPG with loweringlevels of FPG. This is because the DRI risk score captures one'sunderlying metabolic defects by including these other predictiveanalytes, which glucose alone does not capture.

Data used to develop and validate DRI scores can be based onretrospective analysis of data from a multi-center and multi-ethnicprospective observational study with nearly 5,000 non-diabetic subjectsat baseline. The patient report for this assay can show a fastingglucose value and a single 5-year diabetes risk prediction rate (whichincludes the risk predictive value of fasting glucose along with theother biomarkers).

Example 6

The Diabetes Risk Index (DRI) model can be calculated using a pluralityof components including at least one lipoprotein component, Valineand/or another branched chain amino acid, and GlycA and/or anotherinflammatory marker. The model can be adjusted to use differentcomponents based on whether the patient is on a statin or other drugtherapy known to impact DRI risk scores and/or based on whether thebiosample is a fasting or non-fasting biosample.

The DRI risk score can be calculated in a plurality of different mannersand filtered before sent to a clinician (or sent to the clinician forcorrect reporting) and patient based on defined patient criteria so thatthe patient is provided the appropriate score for the test andpatient-specific parameters for that biosample.

Example 7

The DRI model is configured to stratify risk in subjects having the sameA1C, oral glucose tolerance or FPG measurement and a different diabetesrisk score. The diabetes risk index score can be a numerical scorewithin a defined score range, with scores associated with a fourthquartile (4Q) or fifth quintile (5Q) of a population norm reflecting anincreased or high risk of developing type 2 diabetes within 5-7 years.Respective subjects that are at increased risk of developing type 2diabetes prior to onset of type-2 diabetes when glucose is in a rangethat is below prediabetes to the high end of prediabetes, e.g., afasting blood glucose (FPG) levels are between 90-125 mg/dL can beidentified. The DRI score provides information to stratify risk insubjects having the same glucose measurement and a different diabetesrisk score based on underlying metabolic issues in different patients.

Example 8

Table 7 lists potential alternative VLDL parameters that may be used ina DRI model based on a logistic regression analysis on VLDL. One or moreof the alternative VLDL parameters may optionally be used with one ormore of the components described above with respect to any of the otherDRI models and/or components thereof described in the Examples and/orDetailed Description part of the specification. Large VLDL may bereferred to as “VLP (V5p+V6p+CHYp)”, which are TRL particles rangingfrom 60-260 nm diameter. Different definitions of “large VLDL” could beused in a DRI model. For example, the chylomicrons could be excluded andmay be called TRL60-140 based on Table 5. Alternatively or in addition,TRL60-120 (without V6-140) may be used in a DRI model.

TABLE 7 Potential VLDL parameters that may be used in a DRI model withother components. MESA N = 210/2038, MESA N = 210/2038, modelunadjusted, model adjusted on glucose glucose 90-110 mg/dL (glucose90-110 mg/dL) X2 X2 parameter X2 parameter X2 (model parameter (modelparameter Logistic regression adjusted (model adjusted (model on NewDiabetes on not- on not- NMR parameters glucose) adjusted) glucose)adjusted) ChyloL (185-260) −0.80 −0.77 ChyloS (170-180) −5.69 0.017−3.90 0.0482 V6tg(2) 100 + 120 5.60 0.0178 4.47 0.0344 V6tg(3) 100 +120 + 3.73 0.0534 3.13 0.0766 140 V5tg 8.31 0.0039 4.96 0.0259 VLP(V5p + V6p + 8.55 0.0035 5.13 0.0235 CHYp) V56tg(2) 9.68 0.0019 6.000.0143 V56tg(3) 9.46 0.0021 5.88 0.0153

The foregoing is illustrative of the present invention and is not to beconstrued as limiting thereof. Although a few exemplary embodiments ofthis invention have been described, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention as defined inthe claims. In the claims, means-plus-function clauses, where used, areintended to cover the structures described herein as performing therecited function and not only structural equivalents but also equivalentstructures. Therefore, it is to be understood that the foregoing isillustrative of the present invention and is not to be construed aslimited to the specific embodiments disclosed, and that modifications tothe disclosed embodiments, as well as other embodiments, are intended tobe included within the scope of the appended claims. The invention isdefined by the following claims, with equivalents of the claims to beincluded therein.

That which is claimed is:
 1. A method of evaluating a subject's risk ofdeveloping type 2 diabetes or all-cause death, comprising: placing an invitro biosample of the subject in an NMR spectrometer, performingNuclear Magnetic Resonance on the at least one in vitro biosample tomeasure at least one lipoprotein component and at least one of ameasurement of (i) GlycA or GlycB or (ii) at least one branched chainamino acid, and programmatically calculating the risk index for diabetesor all-cause death of the subject using at least one definedmathematical model that includes a measurement of at least onelipoprotein component and at least one of the measurement of GlycA orGlycB, wherein GlycA is a composite Nuclear Magnetic Resonance signalfrom carbohydrate portions of acute phase reactant glycoproteinscontaining N-acetylglucosamine and/or N-acetylgalactosamine moietiesthat is detectable at about 2.00 ppm when Nuclear Magnetic Resonancemeasurements are performed at about 47 degrees C., and wherein GlycB isa composite NMR signal from the carbohydrate portions of acute phasereactant glycoproteins containing N-acetylneuraminic acid (sialic acid)moieties, more particularly from the protons of the 5-N acetyl methylgroups that is detectable at about 2.04 ppm when Nuclear MagneticResonance measurements are performed at about 47 degrees C.
 2. Themethod of claim 1, wherein one or more of the at least one lipoproteincomponent forms a numerator, denominator or multiplication factor of atleast one interaction parameter.
 3. The method of claim 1, wherein thedefined mathematical model of risk comprises GlycA, wherein the at leastone lipoprotein component comprises an interaction parameter defined bythe measurement of GlycA multiplied by a concentration of a definedsubpopulation of high density lipoprotein (HDL) particles.
 4. The methodof claim 1, wherein the at least one lipoprotein component of thedefined mathematical model of risk comprises a first interactionparameter of the measurement of GlycA multiplied by a concentration of adefined subpopulation of high density lipoprotein (HDL) particles andwherein the HDL subpopulation comprises only medium HDL particlesubclasses with diameters between about 8.3 nm (average) to about 10.0nm (average).
 5. The method of claim 1, further comprisingprogrammatically generating a report with a graph of risk of progressionto type 2 diabetes in the future versus ranges of glucose levels with avisual indication of higher and lower risk values with respect to thecalculated diabetes risk index for ease of identifying or understandingrisk stratification for a particular glucose level.
 6. The method ofclaim 1, wherein the at least one defined mathematical model of riskincludes NMR derived measurements of GlycA and Valine and at least oneinteraction parameter comprising a high density lipoprotein (HDL)particle subpopulation.
 7. The method of claim 1, wherein the definedmathematical risk model includes only NMR derived measurements of arespective subject's at least one in vitro blood plasma or serumbiosample.
 8. The method of claim 1, further comprising, before theprogrammatic calculation, placing the in vitro biosample of the subjectin an NMR spectrometer; obtaining at least one NMR spectrum of thebiosample; deconvolving the obtained at least one NMR spectrum; andcalculating NMR derived measurements of GlycA and a plurality ofselected lipoprotein subclasses based on the deconvolved at least oneNMR spectrum.
 9. The method of claim 8, further comprising calculating ameasurement of Valine as one of or as the only branched chain aminoacid.
 10. The method of claim 1, wherein the at least one lipoproteincomponent comprises at least one interaction parameter that includes aconcentration of a subpopulation of high density lipoprotein (HDL)particles as a multiplication factor or a numerator or denominator of aratio.
 11. A circuit configured to determine whether a patient isat-risk for developing type 2 diabetes or all-cause death and/or whethera patient has prediabetes, comprising: at least one processor configuredto electronically calculate a risk index based on at least onemathematical model of risk to convergence to type 2 diabetes orall-cause death that considers a measurement of at least one lipoproteincomponent and a measurement of either (i) GlycA or (ii) at least onebranched chain amino acid and GlycA, from at least one in vitrobiosample of the subject, wherein GlycA is a composite Nuclear MagneticResonance signal from carbohydrate portions of acute phase reactantglycoproteins containing N-acetylglucosamine and/orN-acetylgalactosamine moieties that is detectable at about 2.00 ppm whenNuclear Magnetic Resonance measurements are performed at about 47degrees C.
 12. The circuit of claim 11, wherein the risk index fordiabetes or all-cause death is a numerical score within a defined scorerange, and wherein the circuit is in communication with or is configuredwith an electronic analysis circuit in communication with a respectivedisplay of remote electronic devices configured to allow a user to enter(i) a glucose value, (ii) a glucose value and a diabetes risk indexscore, or (iii) a diabetes risk index score, and wherein the circuit isconfigured to use a corresponding glucose value and a diabetes riskindex score of a patient to stratify a risk of developing type 2diabetes in the future for patients having the same glucose value whenthe glucose value is in an intermediate risk range associated with whenfasting plasma glucose levels are between 90-110 mg/dL, A1C % levels arebetween 5.7-6.4 or oral glucose tolerance levels are between 140-199mg/dL.
 13. The circuit of claim 11, wherein the at least onemathematical model of risk includes an NMR derived measurement of GlycAalong with an NMR measurement of Valine, with Valine as the at least onebranched chain amino acid, and wherein the at least one lipoproteincomponent includes a least one interaction parameter comprising aconcentration of a sub-population of high density lipoprotein (HDL)particles.
 14. The circuit of claim 11, wherein the defined mathematicalmodel of risk comprises GlycA, and wherein the at least one lipoproteincomponent comprises an interaction parameter of a measurement of GlycAmultiplied by concentration of a defined subpopulation of high densitylipoprotein (HDL) particles.
 15. The circuit of claim 14, wherein thedefined HDL subpopulation includes only medium HDL particle subclasseswith diameters between 8.3 nm (average) to about 10.0 nm (average). 16.The circuit of claim 11, wherein the at least one processor isconfigured to generate a diabetes risk index as a numerical score with adefined range with a scores at a high end of the scale representingincreased risk, and wherein the at least one processor is configured togenerate a report with a graph of risk of progression to type 2 diabetesin the future versus ranges of glucose levels and a comparative scale ofrisk associated with the diabetes risk index score.
 17. The circuit ofclaim 16, wherein the graph includes visual references of at leastcomparative low risk DRI scores associated with a first quartile, firstquintile or first decile of DRI scores and high risk DRI scoresassociated with a fourth quartile, fifth quintile or 10^(th) deciles ofDRI scores based on a defined population to thereby allow for ease ofidentifying or understanding risk stratification.
 18. A computer programproduct for evaluating in vitro patient biosamples, the computer programproduct comprising: a non-transitory computer readable storage mediumhaving computer readable program code embodied in the medium, thecomputer-readable program code comprising: computer readable programcode that provides at least one mathematical model of risk toprogression to type 2 diabetes in the future, wherein the at least onemathematical model of risk to progression to type 2 diabetes includes aplurality of components, including at least one lipoprotein component,at least one inflammatory biomarker and at least one branched chainamino acid; and computer readable program code that calculates adiabetes risk index associated with a patient's biosample based on theat least one mathematical model of a risk of developing type 2 diabetes,wherein the defined mathematical model of risk comprises GlycA or GlycBas the inflammatory biomarker, and wherein the at least one lipoproteincomponent includes an interaction parameter of a measurement of GlycA orGlycB multiplied by a concentration of a defined subpopulation of highdensity lipoprotein (HDL) particles, wherein GlycA is a compositeNuclear Magnetic Resonance signal from carbohydrate portions of acutephase reactant glycoproteins containing N-acetylglucosamine and/orN-acetylgalactosamine moieties that is detectable at about 2.00 ppm whenNuclear Magnetic Resonance measurements are performed at about 47degrees C., and wherein GlycB is a composite NMR signal from thecarbohydrate portions of acute phase reactant glycoproteins containingN-acetylneuraminic acid (sialic acid) moieties, more particularly fromthe protons of the 5-N acetyl methyl groups that is detectable at about2.04 ppm when Nuclear Magnetic Resonance measurements are performed atabout 47 degrees C.
 19. The computer program product of claim 18,wherein the computer readable program code that provides the at leastone mathematical model includes model components of NMR derivedmeasurements of GlycA as the inflammatory marker and Valine as the atleast one branched chain amino acid.
 20. The computer program product ofclaim 18, wherein the defined HDL subpopulation includes only medium HDLparticle subclasses with diameters between 8.3 nm (average) to about10.0 nm (average).
 21. A system, comprising: an NMR spectrometer foracquiring at least one NMR spectrum of an in vitro biosample; and atleast one processor in communication with the NMR spectrometer, the atleast one processor configured to determine, for a respective biosampleusing the acquired at least one NMR spectrum, a risk index score fordiabetes or all-cause death based on at least one defined mathematicalmodel of risk to convergence to type 2 diabetes that includes GlycA orGlycB, at least one interaction parameter comprising GlycA or GlycB andat least one lipoprotein component, and at least one branched chainamino acid obtained from at least one in vitro biosample of the subject,and wherein the at least one lipoprotein component of the interactionparameter includes a concentration of a defined subpopulation of highdensity lipoprotein (HDL) particles.
 22. An NMR system comprising: a NMRspectrometer; a flow probe in communication with the spectrometer; andat least one processor in communication with the spectrometer configuredto obtain (i) NMR signal of a defined GlycA or GlycB fitting region ofNMR spectra associated with GlycA or GlycB of a blood plasma or serumspecimen in the flow probe; (ii) NMR signal of a defined branched chainamino acid fitting region of NMR spectra associated with the specimen inthe flow probe; and (iii) NMR signal of lipoprotein subclasses; whereinthe at least one processor is configured to calculate measurements of(i) GlycA or GlycB, (ii) at least one branched chain amino acid and(iii) the lipoprotein subclasses using the NMR signals, and wherein theat least one processor is configured to calculate a diabetes risk indexthat uses the calculated measurements of at least one of GlycA or GlycB,the at least one branched chain amino acid and some of the lipoproteinsubclasses wherein GlycA is a composite Nuclear Magnetic Resonancesignal from carbohydrate portions of acute phase reactant glycoproteinscontaining N-acetylglucosamine and/or N-acetylgalactosamine moietiesthat is detectable at about 2.00 ppm when Nuclear Magnetic Resonancemeasurements are performed at about 47 degrees C., and wherein GlycB isa composite NMR signal from the carbohydrate portions of acute phasereactant glycoproteins containing N-acetylneuraminic acid (sialic acid)moieties, more particularly from the protons of the 5-N acetyl methylgroups that is detectable at about 2.04 ppm when Nuclear MagneticResonance measurements are performed at about 47 degrees C.
 23. Thesystem of claim 22, wherein the at least one interaction parameterincludes a first interaction parameter defined by a measurement of GlycAmultiplied by a concentration of a defined subpopulation of high densitylipoprotein (HDL) particles.
 24. The system of claim 22, wherein the atleast one processor calculates a concentration of a defined HDLsubpopulation using HDL subclasses for at least one interactionparameter, wherein the HDL subpopulation comprises only medium HDLparticle subclasses with diameters between 8.3 nm (average) to about10.0 nm (average).
 25. A method of monitoring a patient to evaluate atherapy or to determine whether the patient is at-risk of developingtype 2 diabetes, comprising: programmatically evaluating a plurality ofNMR derived measurements of selected lipoprotein subclasses and at leastone of (i) GlycA or GlycB or (ii) at least one branched chain amino acidand at least one of GlycA or GlycB, of at least one patient in vitrobiosample; programmatically calculating a diabetes risk index ofrespective patients using the NMR derived, measurements; and evaluatingat least one of (i) whether the diabetes risk index is above a definedlevel of a population norm associated with increased risk of developingtype 2 diabetes; and/or (ii) whether the diabetes risk index isincreasing or decreasing over time to thereby evaluate change in riskstatus that may be in response to a therapy, wherein GlycA is acomposite Nuclear Magnetic Resonance signal from carbohydrate portionsof acute phase reactant glycoproteins containing N-acetylglucosamineand/or N-acetylgalactosamine moieties that is detectable at about 2.00ppm when Nuclear Magnetic Resonance measurements are performed at about47 degrees C. wherein GlycB is a composite NMR signal from thecarbohydrate portions of acute phase reactant glycoproteins containingN-acetylneuraminic acid (sialic acid) moieties, more particularly fromthe protons of the 5-N acetyl methyl groups that is detectable at about2.04 ppm when Nuclear Magnetic Resonance measurements are performed atabout 47 degrees C.